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Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

An improved forensic-by-design framework for cloud computing with systems engineering standard compliance

Reliability of trust management systems in cloud computing.

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

Cloud Computing Adoption in the Construction Industry of Singapore: Drivers, Challenges, and Strategies

An extensive review of web-based multi granularity service composition.

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.

Integrated Blockchain and Cloud Computing Systems: A Systematic Survey, Solutions, and Challenges

Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.

Cloud Computing and Undergraduate Researches in Universities in Enugu State: Implication for Skills Demand

Cloud building block chip for creating fpga and asic clouds.

Hardware-accelerated cloud computing systems based on FPGA chips (FPGA cloud) or ASIC chips (ASIC cloud) have emerged as a new technology trend for power-efficient acceleration of various software applications. However, the operating systems and hypervisors currently used in cloud computing will lead to power, performance, and scalability problems in an exascale cloud computing environment. Consequently, the present study proposes a parallel hardware hypervisor system that is implemented entirely in special-purpose hardware, and that virtualizes application-specific multi-chip supercomputers, to enable virtual supercomputers to share available FPGA and ASIC resources in a cloud system. In addition to the virtualization of multi-chip supercomputers, the system’s other unique features include simultaneous migration of multiple communicating hardware tasks, and on-demand increase or decrease of hardware resources allocated to a virtual supercomputer. Partitioning the flat hardware design of the proposed hypervisor system into multiple partitions and applying the chip unioning technique to its partitions, the present study introduces a cloud building block chip that can be used to create FPGA or ASIC clouds as well. Single-chip and multi-chip verification studies have been done to verify the functional correctness of the hypervisor system, which consumes only a fraction of (10%) hardware resources.

Study On Social Network Recommendation Service Method Based On Mobile Cloud Computing

Cloud-based network virtualization in iot with openstack.

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

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Advances, Systems and Applications

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  • Published: 06 August 2022

Big data analytics in Cloud computing: an overview

  • Blend Berisha 1 ,
  • Endrit Mëziu 1 &
  • Isak Shabani 1  

Journal of Cloud Computing volume  11 , Article number:  24 ( 2022 ) Cite this article

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Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot of variety. Big Data is a concept that deals with storing, processing and analyzing large amounts of data. Cloud computing on the other hand is about offering the infrastructure to enable such processes in a cost-effective and efficient manner. Many sectors, including among others businesses (small or large), healthcare, education, etc. are trying to leverage the power of Big Data. In healthcare, for example, Big Data is being used to reduce costs of treatment, predict outbreaks of pandemics, prevent diseases etc. This paper, presents an overview of Big Data Analytics as a crucial process in many fields and sectors. We start by a brief introduction to the concept of Big Data, the amount of data that is generated on a daily bases, features and characteristics of Big Data. We then delve into Big Data Analytics were we discuss issues such as analytics cycle, analytics benefits and the movement from ETL to ELT paradigm as a result of Big Data analytics in Cloud. As a case study we analyze Google’s BigQuery which is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. As a Platform as a Service (PaaS) supports querying using ANSI SQL. We use the tool to perform different experiments such as average read, average compute, average write, on different sizes of datasets.

Introduction

We live in the data age. We see them everywhere and this is due to the great technological developments that have taken place in recent years. The rate of digitalization has increased significantly and now we are rightly talking about” digital information societies”. If 20 or 30 years ago only 1% of the information produced was digital, now over 94% of this information is digital and it comes from various sources such as our mobile phones, servers, sensor devices on the Internet of Things, social networks, etc. [ 1 ]. The year 2002 is considered the” beginning of the digital age” where an explosion of digitally produced equipment and information was seen.

The number and amount of information collected has increased significantly due to the increase of devices that collect this information such as mobile devices, cheap and numerous sensor devices on the Internet of Things (IoT), remote sensing, software logs, cameras, microphones, RFID readers, wireless sensor networks, etc. [ 2 ]. According to statistics, the amount of data generated / day is about 44 zettabytes (44 × 10 21 bytes). Every second, 1.7 MB of data is generated per person [ 3 ]. Based on International Data Group forecasts, the global amount of data will increase exponentially from 2020 to 2025, with a move from 44 to 163 zettabytes [ 4 ]. Figure  1 shows the amount of global data generated, copied and consumed. As can be seen, in the years 2010–2015, the rate of increase from year to year has been smaller, while since 2018, this rate has increased significantly thus making the trend exponential in nature [ 3 ].

figure 1

Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2024 (estimated) [ 3 ]

To get a glimpse of the amount of data that is generated on a daily basis, let’s see a portion of data that different platforms produce. On the Internet, there is so much information at our fingertips. We add to the stockpile everytime we look for answers from our search engines. As a results Google now produces more than 500,000 searches every second (approximately 3.5 billion search per day) [ 5 ]. By the time of writing this article, this number must have changed! Social media on the other hand is a massive data producer. 

People’s ‘love affair’ with social media certainly fuels data creation. Every minute, Snapchat users share 527,760 photos, more than 120 professionals join LinkedIn, users watch 4,146,6000 Youtube videos, 456,000 are sent to Twitter and Instagram users post 46,740 photos [ 5 ]. Facebook remains the largest social media platform, with over 300 million photos uploaded every day with more than 510,000 comments posted and 293,000 statuses updated every minute.

With the increase in the number and quantity of data, there have been advantages but also challenges as systems for managing relational databases and other traditional systems have difficulties in processing and analyzing this quantity. For this reason, the term ‘big data’ arose not only to describe the amount of data but also the need for new technologies and ways of processing and analyzing this data. Cloud Computing has facilitated data storage, processing and analysis. Using Cloud we have access to almost limitless storage and computer power offered by different vendors. Cloud delivery models such as: IAAS (Infrastructure as a Service), PAAS (Platform as a Service) can help organisations across different sectors handle Big Data easier and faster. The aim of this paper is to provide an overview of how analytics of Big Data in Cloud Computing can be done. For this we use Google’s platform BigQuery which is a serverless data warehouse with built-in machine learning capabilities. It’s very robust and has plenty of features to help with the analytics of different size and type of data.

What is big data?

Many authors and organizations have tried to provide a definition of ‘Big Data’. According to [ 6 ] “Big Data refers to data volumes in the range of exabytes and beyond”. In Wikipedia [ 7 ] big data is defined as an accumulation of datasets so huge and complex that it becomes hard to process using database management tools or traditional data processing applications, while the challenges include capture, storage, search, sharing, transfer, analysis, and visualization.

Sam Madden from Massachusetts Institute of Technology (MIT) considers” Big Data” to be data that is too big, too fast, or too hard for existing tools to process [ 8 ]. By too big, it means data that is at the petabyte level and that comes from various sources. By ‘too fast’ it means data growth which is fast and should also be processed quickly. By too hard it means the difficulty that arises as a result the data not adapting to the existing processing tools [ 9 ]. In PCMag (one of the most popular journals on technological trends), Big data refers to the massive amounts of data that is collected over time that are difficult to analyze and handle using common database management tools [ 10 ]. There are many other definitions for Big Data, but we consider that these are enough to gain an impression on this concept.

Features and characteristics of big data

One question that researchers have struggled to answer is what might qualify as ‘big data’? For this reason, in 2001 industry analyst Doug Laney from Gartner introduced the 3 V model which are three features that must complement the data to be considered” big data”: volume, velocity, variety . Volume is a property or characteristic that determines the size of data, usually reported in Terabyte or Petabyte. For example, social networks like Facebook store among others photos of users. Due to the large number of users, it is estimated that Facebook stores about 250 billion photos and over 2.5 trillion posts of its users. This is an extremely large amount of data that needs to be stored and processed. Volume is the most representative feature of ‘big data’ [ 8 ]. In terms of volume, tera or peta level data is usually considered ‘big’ although this depends on the capacity of those analyzing this data and the tools available to them [ 8 ]. Figure  2 shows what each of the three V's represent.

figure 2

3 V’s of Big Data [ 6 ]

The second property or characteristic is velocity . This refers to the degree to which data is generated or the speed at which this data must be processed and analyzed [ 8 ]. For example, Facebook users upload more than 900 million photos a day, which is approximately 104 uploaded photos per second. In this way, Facebook needs to process, store and retrieve this information to its users in real time. Figure  3 shows some statistics obtained from [ 11 ] which show the speed of data generation from different sources. As can be seen, social media and the Internet of Things (IoT) are the largest data generators, with a growing trend.

figure 3

Examples of the velocity of Big Data [ 9 ]

There are two main types of data processing: batch and stream. In batch, processing happens in blocks of data that have been stored over a period of time. Usually data processed in batch are big, so they will take longer to process. Hadoop MapReduce is considered to be the best framework for processing data in batches [ 11 ]. This approach works well in situations where there is no need for real-time analytics and where it is important to process large volumes of data to get more detailed insights.

Stream processing, on the other hand, is a key to the processing and analysis of data in real time. Stream processing allows for data processing as they arrive. This data is immediately fed into analytics tools so the results are generated instantly. There are many scenarios where such an approach can be useful such as fraud detection, where anomalies that signal fraud are detected in real time. Another use case would be online retailers, where real-time processing would enable them to compile large histories of costumer interactions so that additional purchases could be recommended for the costumers in real time [ 11 ].

The third property is variety , which refers to different types of data which are generated from different sources. “Big Data” is usually classified into three major categories: structured data (transactional data, spreadsheets, relational databases etc.), semi-structured (Extensible Markup Language - XML, web server logs etc) and unstructured (social media posts, audio, images, video etc.). In the literature, as a fourth category is also mentioned ‘meta-data’ which represents data about data. This is also shown in Fig.  4 . Most of the data today belong to the category of unstructured data (80%) [ 11 ].

figure 4

Main categories of data variety in Big Data [ 9 ]

Over time, the tree features of big data have been complemented by two additional ones: veracity and value . Veracity is equivalent to quality, which means data that are clean and accurate and that have something to offer [ 12 ]. The concept is also related to the reliability of data that is extracted (e.g., costumer sentiments in social media are not highly reliable data). Value of the data is related to the social or economic value data can generate. The degree of value data can produce depends also on the knowledge of those that make use of it.

Big data analytics in cloud computing

Cloud Computing is the delivery of computing services such as servers, storage, databases, networking, software, analytics etc., over the Internet (“the cloud”) with the aim of providing flexible resources, faster innovation and economies of scale [ 13 ]. Cloud computing has revolutionized the way computing infrastructure is abstracted and used. Cloud paradigms have been extended to include anything that can be considered as a service (hence x a service). The many benefits of cloud computing such as elasticity, pay-as-you-go or pay-per-use model, low upfront investment etc., have made it a viable and desirable choice for big data storage, management and analytics [ 13 ]. Because big data is now considered vital for many organizations and fields, service providers such as Amazon, Google and Microsoft are offering their own big data systems in a cost-efficient manner. These systems offer scalability for business of all sizes. This had led to the prominence of the term Analytics as a Service (AaaS) as a faster and efficient way to integrate, transform and visualize different types of data. Data Analytics.

Big data analytics cycle

According to [ 14 ] processing big data for analytics differs from processing traditional transactional data. In traditional environments, data is first explored then a model design as well as a database structure is created. Figure  5 . depicts the flow of big data analysis. As can be seen, it starts by gathering data from multiple sources, such as multiple files, systems, sensors and the Web. This data is then stored in the so called” landing zone” which is a medium capable of handling the volume, variety and velocity of data. This is usually a distributed file system. After data is stored, different transformations occur in this data to preserve its efficiency and scalability. Afer that, they are integrated into particular analytical tasks, operational reporting, databases or raw data extracts [ 14 ].

figure 5

Flow in the processing of Big Data [ 11 ]

Moving from ETL to ELT paradigm

ETL (Extract, Transform, Load) is about taking data from a data source, applying the transformations that might be required and then load it into a data warehouse to run reports and queries against them. The downside of this approach or paradigm is that is characterized by a lot of I/O activity, a lot of string processing, variable transformation and a lot of data parsing [ 15 ].

ELT (Extract, Load, Transform) is about taking the most compute-intensive activity (transformation) and doing it not in an on-premise service which is already under pressure with regular transaction-handling but instead taking it to the cloud [ 15 ]. This means that there is no need for data staging because data warehousing solution is used for different types.

of data including those that are structured, semi-structured, unstructured and raw. This approach employs the concept of” data lakes” that are different from OLAP (Online Analytical Processing) data warehouses because they do not require the transformation of data before loading them [ 15 ]. Figure 6 illustrates the differences between the two paradigms. As seen, the main difference is where transformation process takes place.

figure 6

Differences between ETL and ELT [ 15 ]

ELT has many benefits over traditional ETL paradigm. The most crucial, as mentioned, is the fact that data of any format can be ingested as soon as it becomes available. Another one is the fact that only the data required for particular analysis can be transformed. In ETL, the entire pipeline and structure of the data in the OLAP may require modification if the previous structure does not allow for new types of analysis [ 16 ].

Some advantages of big data analytics

As mentioned, companies across various sectors in the industry are leveraging Big Data in order to promote decision making that is data-driven. Besides tech industry, the usage and popularity of Big Data has expanded to include healthcare, governance, retail, supply chain management, education etc. Some of the benefits of Big Data Analytics mentioned in [ 17 ] include:

Data accumulation from different sources including the Internet, online shopping sites, social media, databases, external third-party sources etc.

Identification of crucial points that are hidden within large datasets in order to influence business decisions.

Identification of the issues regarding systems and business processes in real time.

Facilitation of service/product delivery to meet or exceed client expecations.

Responding to customer requests, queries and grievances in real time.

Some other benefits according to [ 16 ] are related to:

Cost optimization - One of the biggest advantages of Big Data tools such as Hadoop or Spark is that they offer cost advantages to businesses regarding the storage, processing and analysis of large amounts of data. Authors mention the logistics industry as an example to highlight the cost-reduction benefits of Big Data. In this industry, the cost of product returns is 1.5 times higher than that of actual shipping costs. With Big Data Analytics, companies can minimize product return costs by predicting the likelihood of product returns. By doing so, they can then estimate which products are most likely to be returned and thus enable the companies to take suitable measures to reduce losses on returns.

Efficiency improvements - Big Data can improve operational efficiency by a margin. Big Data tools can amass large amounts of useful costumer data by interacting and gaining their feedback. This data can then be analyzed and interpreted to extract some meaningful patterns hidden within such as customer taste and preferences, buying behaviors etc. This in turn allows companies to create personalized or tailored products/services.

Innovation - Insights from Big Data can be used to tweak business strategies, develop new products/services, optimize service delivery, improve productivity etc. These can all lead to more innovation.

As seen, Big Data Analytics has been mostly leveraged by businesses, but other sectors have also benefited. For example, in healthcare many states are now utilizing the power of Big Data to predict and also prevent epidemics, cure diseases, cut down costs etc. This data has also been used to establish many efficient treatment models. With Big Data more comprehensive reports were generated and these were then converted into relevant critical insights to provide better care [ 17 ].

In education, Big Data has also been used extensively. They have enabled teachers to measure, monitor and respond in real-time to student’s understanding of the material. Professors have created tailor-made materials for students with different knowledge levels to increase their interest [ 18 ].

Case study: GOOGLE’S big query for data processing and analytics

Google Cloud Platform contains a number of services designed to analyze and process big data. Throughout this paper we have described and discussed the architecture and main components of Biguery as one of the most used big data processing tools in GCP. BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports querying using ANSI SQL. It also has built-in machine learning capabilities. Since its launch in 2011 it has gained a lot of popularity and many big companies have utilized it for their data analytics [ 19 ].

From a user perspective, BigQuery has an intuitive user interface which can be accessed in a number of ways depending on user needs. The simplest way to interact with this tool is to use its graphical web interface as shown in Fig.  7 . Slightly more complicated but faster approaches include using cloud console or Bigquery APIs. From Fig. 7 Bigquery web interface offers you the options to add or select existing datasets, schedule and construct queries or transfer data and display results.

figure 7

BigQuery Interface

Data processing and query construction occurs under the sql workspace section, Bigquery offers a rich sql-like syntax to compute and process large sets of data, it operates on relational datasets with well-defined structure including tables with specified columns and types. Figure  8 shows a simple query construction syntax and highlights its execution details. Data displayed under query results shows main performance components of the executed query starting from elapsed time, consumed slot time, size of data processed, average and maximum wait, write and compute times. Query defined in Fig.  8 combines three datasets which contain information regarding Covid-19 reported cases, deaths and recoveries from more than 190 countries through year 2020 till January 2021. Google BigQuery is flexible in a way that allows you to use and combine various datasets suitable for your task easily and with small delays. It contains an ever growing list of public datasets at your disposal and also offers the options to create, edit and import your own. Figure  9 shows the process of adding a table to the newly created dataset. From the Fig.  9 , we see that for table creation as a source we have used a local csv file, this file will be used to create table schema and populate it with data, aside from local upload option as a source to create the table we can use Google BigTable, Google Cloud Storage or Google Drive. The newly created table with its respective data then is ready to be used to construct queries and obtain new insights as shown in Fig. 8 .

figure 8

BigQuery execution details

figure 9

Adding table to the created dataset

One advantage of using imported data in the cloud is the option to manage its access and visibility in the cloud project and cloud members scope. Depending from the way of use, queried data can be saved directly to the local computer through the use of “save results” option from Fig. 8 which offers a variety of formats and data extensions settings to choose from but can also be explored in different configurations using “explore data” option. You can also save constructed queries for later use or schedule query execution interval for more accurate data transmutation through API endpoints. Figure 10 shows how much the average compute time will change/increase with the increase in the size of the dataset used.

figure 10

Average compute time dependence in dataset size

Experiments with different dataset sizes

Before moving to data exploration lets analyze performance results of BigQuery in simple queries with variable dataset sizes. In Table  1 we have shown the query execution details of five simple select queries done on five different datasets. The results are displayed against six different performance categories, from the data we see a correlation between size of the dataset and its average read, write and compute.

From the graph we see that the dependence between dataset size and average compute size is exponential, meaning that with the increase in data size, average compute time is exponentially increased.

Data returned from constructed queries aside from being displayed in a simple tabular form or as a JSON object can also be transferred to data studio which is an integrated tool to better display and visualize gathered information. One way of displaying queried data from Fig. 8 with data studio tool is shown in Fig.  11 . In this case a bar table chart visualization option is chosen.

figure 11

Using data studio for data visualization

Big Data is not a new term but has gained its spotlight due to the huge amounts of data that are produced daily from different sources. From our analysis we saw that big data is increasing in a fast pace, leading to benefits but also challenges. Cloud Computing is considered to be the best solution for storing, processing and analyzing Big Data. Companies like Amazon, Google and Microsoft offer their public services to facilitate the process of dealing with Big Data. From the analysis we saw that there are multiple benefits that Big Data analytics provides for many different fields and sectors such as healthcare, education and business. We also saw that because of the interaction of Big Data with Cloud Computing there is a shift in the way data is processed and analyzed. In traditional settings, ETL is used whereas in Big Data, ELT is used. We saw that the latter has clear advantages when compared to the former.

From our case study we saw that BigQuery is very good for running complex analytical queries, which means there is no point in running queries that are doing simple aggregation or filtering. BigQuery is suitable for heavy queries, those that operate using a big set of data. The bigger the dataset, the more it is likely to gain in performance. This is when compared to the traditional relational databases,as BigQuery implements different parallel schemas to speed up the execution time.

BigQuery doesn’t like joins and merging data into one table gets a better execution time. It is good for scenarios where data does not change often as it has built-in cache. BigQuery can also be used when one wants to reduce the load on the relational database as it offers different options and configurations to improve query performance. Also pay as you go service can be used where charges are made based on usage or flat rate service which offers a specific slot rate and charges in daily, monthly or yearly plan.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request. The authors declare that they have no funder.

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Acknowledgements

The authors would like to thank the colleageous and professors from the University of Prishtina for their insightful comments and suggestions that helped in improving the quality of the paper.

The authors declare that they have no funder.

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Faculty of Electrical and Computer Engineering, Department of Computer Engineering, University of Prishtina, 10000, Prishtina, Kosovo

Blend Berisha, Endrit Mëziu & Isak Shabani

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Blend Berisha wrote the Introduction, Features and characteristics of Big Data and Conclusions. Endrit Meziu wrote Big Data¨ Analytics in Cloud Computing and part of the case study. Isak Shabani has contributed in the methodology, resources and in supervising the work process. All authors prepared the figures and also reviewed the manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Isak Shabani .

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Berisha, B., Mëziu, E. & Shabani, I. Big data analytics in Cloud computing: an overview. J Cloud Comp 11 , 24 (2022). https://doi.org/10.1186/s13677-022-00301-w

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Published : 06 August 2022

DOI : https://doi.org/10.1186/s13677-022-00301-w

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Top 10 Cloud Computing Research Topics in 2020

Cloud computing has suddenly seen a spike in employment opportunities around the globe with tech giants like Amazon, Google, and Microsoft hiring people for their cloud infrastructure. Before the onset of cloud computing, companies and businesses had to set up their own data centers, allocate resources and other IT professionals thereby increasing the cost. The rapid development of the cloud has led to more flexibility, cost-cutting, and scalability. 

Top-10-Cloud-Computing-Research-Topics-in-2020

The Cloud Computing market its an all-time high with the current market size at USD 371.4 billion and is expected to grow up to USD 832.1 billion by 2025! It’s quickly evolving and gradually realizing its business value along with attracting more and more researchers, scholars, computer scientists, and practitioners. Cloud computing is not a single topic but a composition of various techniques which together constitute the cloud. Below are 10 the most demanded research topics in the field of cloud computing:

1. Big Data

Big data refers to the large amounts of data produced by various programs in a very short duration of time. It is quite cumbersome to store such huge and voluminous amounts of data in company-run data centers. Also, gaining insights from this data becomes a tedious task and takes a lot of time to run and provide results, therefore cloud is the best option. All the data can be pushed onto the cloud without the need for physical storage devices that are to be managed and secured. Also, some popular public clouds provide comprehensive big data platforms to turn data into actionable insights. 

DevOps is an amalgamation of two terms, Development and Operations. It has led to Continuous Delivery, Integration, and Deployment and therefore reducing boundaries between the development team and the operations team. Heavy applications and software need elaborate and complex tech stacks that demand extensive labor to develop and configure which can easily be eliminated by cloud computing. It offers a wide range of tools and technologies to build, test, and deploy applications with a few minutes and a single click. They can be customized as per the client requirements and can be discarded when not in use hence making the process seamless and cost-efficient for development teams.

3. Cloud Cryptography

Data in the cloud is needed to be protected and secured from foreign attacks and breaches. To accomplish this, cryptography in the cloud is a widely used technique to secure data present in the cloud. It allows users and clients to easily and reliably access the shared cloud services since all the data is secured using either the encryption techniques or by using the concept of the private key. It can make the plain text unreadable and limits the view of the data being transferred. Best cloud cryptographic security techniques are the ones that do not compromise the speed of data transfer and provide security without delaying the exchange of sensitive data. 

4. Cloud Load Balancing

It refers to splitting and distributing the incoming load to the server from various sources. It permits companies and organizations to govern and supervise workload demands or application demands by redistributing, reallocating, and administering resources between different computers, networks, or servers. Cloud load balancing encompasses holding the circulation of traffic and demands that exist over the Internet. This reduces the problem of sudden outages, results in an improvement in overall performance, has rare chances of server crashes, and also provides an advanced level of security. Cloud-based servers farms can accomplish more precise scalability and accessibility using the server load balancing mechanism. Due to this, the workload demands can be easily distributed and controlled.

5. Mobile Cloud Computing

It is a mixture of cloud computing, mobile computing, and wireless network to provide services such as seamless and abundant computational resources to mobile users, network operators, and cloud computing professionals. The handheld device is the console and all the processing and data storage takes place outside the physical mobile device. Some advantages of using mobile cloud computing are that there is no need for costly hardware, battery life is longer, extended data storage capacity and processing power improved synchronization of data and high availability due to “store in one place, accessible from anywhere”. The integration and security aspects are taken care of by the backend that enables support to an abundance of access methods. 

6. Green Cloud Computing

The major challenge in the cloud is the utilization of energy-efficient and hence develop economically friendly cloud computing solutions. Data centers that include servers, cables, air conditioners, networks, etc. in large numbers consume a lot of power and release enormous quantities of Carbon Dioxide in the atmosphere. Green Cloud Computing focuses on making virtual data centers and servers to be more environmentally friendly and energy-efficient. Cloud resources often consume so much power and energy leading to a shortage of energy and affecting the global climate. Green cloud computing provides solutions to make such resources more energy efficient and to reduce operational costs. This pivots on power management, virtualization of servers and data centers, recycling vast e-waste, and environmental sustainability. 

7. Edge Computing

It is the advancement and a much more efficient form of Cloud computing with the idea that the data is processed nearer to the source. Edge Computing states that all of the computation will be carried out at the edge of the network itself rather than on a centrally managed platform or the data warehouses. Edge computing distributes various data processing techniques and mechanisms across different positions. This makes the data deliverable to the nearest node and the processing at the edge. This also increases the security of the data since it is closer to the source and eliminates late response time and latency without affecting productivity.

8. Containerization

Containerization in cloud computing is a procedure to obtain operating system virtualization. The user can work with a program and its dependencies utilizing remote resource procedures. The container in cloud computing is used to construct blocks, which aid in producing operational effectiveness, version control, developer productivity, and environmental stability. The infrastructure is upgraded since it provides additional control over the granular activities over the resources. The usage of containers in online services assists storage with cloud computing data security, elasticity, and availability. Containers provide certain advantages such as a steady runtime environment, the ability to run virtually anywhere, and the low overhead compared to virtual machines. 

9. Cloud Deployment Model

There are four main cloud deployment models namely public cloud, private cloud, hybrid cloud, and community cloud. Each deployment model is defined as per the location of the infrastructure. The public cloud allows systems and services to be easily accessible to the general public. Public cloud could also be less reliable since it is open to everyone e.g. Email. A private cloud allows systems and services to be accessible inside an organization with no access to outsiders. It offers better security due to its access restrictions. Hybrid cloud is a mixture of private and public clouds with the critical activities being performed using private cloud and non-critical activities being performed using the public cloud. Community cloud allows system and services to be accessible by a group of an organization.

10. Cloud Security

Since the number of companies and organizations using cloud computing is increasing at a rapid rate, the security of the cloud is a major concern. Cloud computing security detects and addresses every physical and logical security issue that comes across all the varied service models of code, platform, and infrastructure. It collectively addresses these services, however, these services are delivered in units, that is, the public, private, or hybrid delivery model. Security in the cloud protects the data from any leakage or outflow, theft, calamity, and removal. With the help of tokenization, Virtual Private Networks, and firewalls data can be secured. 

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Top 10 Cloud Computing Research Topics of 2024

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Cloud computing is a fast-growing area in the technical landscape due to its recent developments. If we look ahead to 2024, there are new research topics in cloud computing that are getting more traction among researchers and practitioners. Cloud computing has ranged from new evolutions on security and privacy with the use of AI & ML usage in the Cloud computing for the new cloud-based applications for specific domains or industries. In this article, we will investigate some of the top cloud computing research topics for 2024 and explore what we get most out of it for researchers or cloud practitioners. To master a cloud computing field, we need to check these Cloud Computing online courses .

Why Cloud Computing is Important for Data-driven Business?

The Cloud computing is crucial for data-driven businesses because it provides scalable and cost-effective ways to store and process huge amounts of data. Cloud-based storage and analytical platform helps business to easily access their data whenever required irrespective of where it is located physically. This helps businesses to take good decisions about their products and marketing plans. 

Cloud computing could help businesses to improve their security in terms of data, Cloud providers offer various features such as data encryption and access control to their customers so that they can protect the data as well as from unauthorized access. 

Few benefits of Cloud computing are listed below: 

  • Scalability: With Cloud computing we get scalable applications which suits for large scale production systems for Businesses which store and process large sets of data.
  • Cost-effectiveness : It is evident that Cloud computing is cost effective solution compared to the traditional on-premises data storage and analytical solutions due to its scaling capacity which leads to saving more IT costs. 
  • Security : Cloud providers offer various security features which includes data encryption and access control, that can help businesses to protect their data from unauthorized access.
  • Reliability : Cloud providers ensure high reliability to their customers based on their SLA which is useful for the data-driven business to operate 24X7. 

Top 10 Cloud Computing Research Topics

1. neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing.

Cloud computing research topics are getting wider traction in the Cloud Computing field. These topics in the paper suggest a multi-objective evolutionary algorithm (NN-MOEA) based on neural networks for dynamic workflow scheduling in cloud computing. Due to the dynamic nature of cloud resources and the numerous competing objectives that need to be optimized, scheduling workflows in cloud computing is difficult. The NN-MOEA algorithm utilizes neural networks to optimize multiple objectives, such as planning, cost, and resource utilization. This research focuses on cloud computing and its potential to enhance the efficiency and effectiveness of businesses' cloud-based workflows.

The algorithm predicts workflow completion time using a feedforward neural network based on input and output data sizes and cloud resources. It generates a balanced schedule by taking into account conflicting objectives and projected execution time. It also includes an evolutionary algorithm for future improvement.

The proposed NN-MOEA algorithm has several benefits, such as the capacity to manage dynamic changes in cloud resources and the capacity to simultaneously optimize multiple objectives. The algorithm is also capable of handling a variety of workflows and is easily expandable to include additional goals. The algorithm's use of neural networks to forecast task execution times is a crucial component because it enables the algorithm to generate better schedules and more accurate predictions.

The paper concludes by presenting a novel multi-objective evolutionary algorithm-based neural network-based approach to dynamic workflow scheduling in cloud computing. In terms of optimizing multiple objectives, such as make span and cost, and achieving a better balance between them, these cloud computing dissertation topics on the proposed NN-MOEA algorithm exhibit encouraging results.

Key insights and Research Ideas:

Investigate the use of different neural network architectures for predicting the future positions of optimal solutions. Explore the use of different multi-objective evolutionary algorithms for solving dynamic workflow scheduling problems. Develop a cloud-based workflow scheduling platform that implements the proposed algorithm and makes it available to researchers and practitioners.

2. A systematic literature review on cloud computing security: threats and mitigation strategies 

This is one of cloud computing security research topics in the cloud computing paradigm. The authors then provide a systematic literature review of studies that address security threats to cloud computing and mitigation techniques and were published between 2010 and 2020. They list and classify the risks and defense mechanisms covered in the literature, as well as the frequency and distribution of these subjects over time.

The paper suggests the data breaches, Insider threats and DDoS attack are most discussed threats to the security of cloud computing. Identity and access management, encryption, and intrusion detection and prevention systems are the mitigation techniques that are most frequently discussed. Authors depict the future trends of machine learning and artificial intelligence might help cloud computing to mitigate its risks. 

The paper offers a thorough overview of security risks and mitigation techniques in cloud computing, and it emphasizes the need for more research and development in this field to address the constantly changing security issues with cloud computing. This research could help businesses to reduce the amount of spam that they receive in their cloud-based email systems.

Explore the use of blockchain technology to improve the security of cloud computing systems. Investigate the use of machine learning and artificial intelligence to detect and prevent cloud computing attacks. Develop new security tools and technologies for cloud computing environments. 

3. Spam Identification in Cloud Computing Based on Text Filtering System

A text filtering system is suggested in the paper "Spam Identification in Cloud Computing Based on Text Filtering System" to help identify spam emails in cloud computing environments. Spam emails are a significant issue in cloud computing because they can use up computing resources and jeopardize the system's security. 

To detect spam emails, the suggested system combines text filtering methods with machine learning algorithms. The email content is first pre-processed by the system, which eliminates stop words and stems the remaining words. The preprocessed text is then subjected to several filters, including a blacklist filter and a Bayesian filter, to identify spam emails.

In order to categorize emails as spam or non-spam based on their content, the system also employs machine learning algorithms like decision trees and random forests. The authors use a dataset of emails gathered from a cloud computing environment to train and test the system. They then assess its performance using metrics like precision, recall, and F1 score.

The findings demonstrate the effectiveness of the proposed system in detecting spam emails, achieving high precision and recall rates. By contrasting their system with other spam identification systems, the authors also show how accurate and effective it is. 

The method presented in the paper for locating spam emails in cloud computing environments has the potential to improve the overall security and performance of cloud computing systems. This is one of the interesting clouds computing current research topics to explore and innovate. This is one of the good Cloud computing research topics to protect the Mail threats. 

Create a stronger spam filtering system that can recognize spam emails even when they are made to avoid detection by more common spam filters. examine the application of artificial intelligence and machine learning to the evaluation of spam filtering system accuracy. Create a more effective spam filtering system that can handle a lot of emails quickly and accurately.

4. Blockchain data-based cloud data integrity protection mechanism 

The "Blockchain data-based cloud data integrity protection mechanism" paper suggests a method for safeguarding the integrity of cloud data and which is one of the Cloud computing research topics. In order to store and process massive amounts of data, cloud computing has grown in popularity, but issues with data security and integrity still exist. For the proposed mechanism to guarantee the availability and integrity of cloud data, data redundancy and blockchain technology are combined.

A data redundancy layer, a blockchain layer, and a verification and recovery layer make up the mechanism. For availability in the event of server failure, the data redundancy layer replicates the cloud data across multiple cloud servers. The blockchain layer stores the metadata (such as access rights) and hash values of the cloud data and access control information

Using a dataset of cloud data, the authors assess the performance of the suggested mechanism and compare it to other cloud data protection mechanisms. The findings demonstrate that the suggested mechanism offers high levels of data availability and integrity and is superior to other mechanisms in terms of processing speed and storage space.

Overall, the paper offers a promising strategy for using blockchain technology to guarantee the availability and integrity of cloud data. The suggested mechanism may assist in addressing cloud computing's security issues and enhancing the dependability of cloud data processing and storage. This research could help businesses to protect the integrity of their cloud-based data from unauthorized access and manipulation.

Create a data integrity protection system based on blockchain that is capable of detecting and preventing data tampering in cloud computing environments. For enhancing the functionality and scalability of blockchain-based data integrity protection mechanisms, look into the use of various blockchain consensus algorithms. Create a data integrity protection system based on blockchain that is compatible with current cloud computing platforms. Create a safe and private data integrity protection system based on blockchain technology.

5. A survey on internet of things and cloud computing for healthcare

This article suggests how recent tech trends like the Internet of Things (IoT) and cloud computing could transform the healthcare industry. It is one of the Cloud computing research topics. These emerging technologies open exciting possibilities by enabling remote patient monitoring, personalized care, and efficient data management. This topic is one of the IoT and cloud computing research papers which aims to share a wider range of information. 

The authors categorize the research into IoT-based systems, cloud-based systems, and integrated systems using both IoT and the cloud. They discussed the pros of real-time data collection, improved care coordination, automated diagnosis and treatment.

However, the authors also acknowledge concerns around data security, privacy, and the need for standardized protocols and platforms. Widespread adoption of these technologies faces challenges in ensuring they are implemented responsibly and ethically. To begin the journey KnowledgeHut’s Cloud Computing online course s are good starter for beginners so that they can cope with Cloud computing with IOT. 

Overall, the paper provides a comprehensive overview of this rapidly developing field, highlighting opportunities to revolutionize how healthcare is delivered. New devices, systems and data analytics powered by IoT, and cloud computing could enable more proactive, preventative and affordable care in the future. But careful planning and governance will be crucial to maximize the value of these technologies while mitigating risks to patient safety, trust and autonomy. This research could help businesses to explore the potential of IoT and cloud computing to improve healthcare delivery.

Examine how IoT and cloud computing are affecting patient outcomes in various healthcare settings, including hospitals, clinics, and home care. Analyze how well various IoT devices and cloud computing platforms perform in-the-moment patient data collection, archival, and analysis. assessing the security and privacy risks connected to IoT devices and cloud computing in the healthcare industry and developing mitigation strategies.

6. Targeted influence maximization based on cloud computing over big data in social networks

Big data in cloud computing research papers are having huge visibility in the industry. The paper "Targeted Influence Maximization based on Cloud Computing over Big Data in Social Networks" proposes a targeted influence maximization algorithm to identify the most influential users in a social network. Influence maximization is the process of identifying a group of users in a social network who can have a significant impact or spread information. 

A targeted influence maximization algorithm is suggested in the paper "Targeted Influence maximization based on Cloud Computing over Big Data in Social Networks" to find the most influential users in a social network. The process of finding a group of users in a social network who can make a significant impact or spread information is known as influence maximization.

Four steps make up the suggested algorithm: feature extraction, classification, influence maximization, and data preprocessing. The authors gather and preprocess social network data, such as user profiles and interaction data, during the data preprocessing stage. Using machine learning methods like text mining and sentiment analysis, they extract features from the data during the feature extraction stage. Overall, the paper offers a promising strategy for maximizing targeted influence using big data and Cloud computing research topics to look into. The suggested algorithm could assist companies and organizations in pinpointing their marketing or communication strategies to reach the most influential members of a social network.

Key insights and Research Ideas: 

Develop a cloud-based targeted influence maximization algorithm that can effectively identify and influence a small number of users in a social network to achieve a desired outcome. Investigate the use of different cloud computing platforms to improve the performance and scalability of cloud-based targeted influence maximization algorithms. Develop a cloud-based targeted influence maximization algorithm that is compatible with existing social network platforms. Design a cloud-based targeted influence maximization algorithm that is secure and privacy-preserving.

7. Security and privacy protection in cloud computing: Discussions and challenges

Cloud computing current research topics are getting traction, this is of such topic which provides an overview of the challenges and discussions surrounding security and privacy protection in cloud computing. The authors highlight the importance of protecting sensitive data in the cloud, with the potential risks and threats to data privacy and security. The article explores various security and privacy issues that arise in cloud computing, including data breaches, insider threats, and regulatory compliance.

The article explores challenges associated with implementing these security measures and highlights the need for effective risk management strategies. Azure Solution Architect Certification course is suitable for a person who needs to work on Azure cloud as an architect who will do system design with keep security in mind. 

Final take away of cloud computing thesis paper by an author points out by discussing some of the emerging trends in cloud security and privacy, including the use of artificial intelligence and machine learning to enhance security, and the emergence of new regulatory frameworks designed to protect data in the cloud and is one of the Cloud computing research topics to keep an eye in the security domain. 

Develop a more comprehensive security and privacy framework for cloud computing. Explore the options with machine learning and artificial intelligence to enhance the security and privacy of cloud computing. Develop more robust security and privacy mechanisms for cloud computing. Design security and privacy policies for cloud computing that are fair and transparent. Educate cloud users about security and privacy risks and best practices.

8. Intelligent task prediction and computation offloading based on mobile-edge cloud computing

This Cloud Computing thesis paper "Intelligent Task Prediction and Computation Offloading Based on Mobile-Edge Cloud Computing" proposes a task prediction and computation offloading mechanism to improve the performance of mobile applications under the umbrella of cloud computing research ideas.

An algorithm for offloading computations and a task prediction model makes up the two main parts of the suggested mechanism. Based on the mobile application's usage patterns, the task prediction model employs machine learning techniques to forecast its upcoming tasks. This prediction is to decide whether to execute a specific task locally on the mobile device or offload the computation of it to the cloud.

Using a dataset of mobile application usage patterns, the authors assess the performance of the suggested mechanism and compare it to other computation offloading mechanisms. The findings demonstrate that the suggested mechanism performs better in terms of energy usage, response time, and network usage.

The authors also go over the difficulties in putting the suggested mechanism into practice, including the need for real-time task prediction and the trade-off between offloading computation and network usage. Additionally, they outline future research directions for mobile-edge cloud computing applications, including the use of edge caching and the integration of blockchain technology for security and privacy. 

Overall, the paper offers a promising strategy for enhancing mobile application performance through mobile-edge cloud computing. The suggested mechanism might improve the user experience for mobile users while lowering the energy consumption and response time of mobile applications. These Cloud computing dissertation topic leads to many innovation ideas. 

Develop an accurate task prediction model considering mobile device and cloud dynamics. Explore machine learning and AI for efficient computation offloading. Create a robust framework for diverse tasks and scenarios. Design a secure, privacy-preserving computation offloading mechanism. Assess computation offloading effectiveness in real-world mobile apps.

9. Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology

Enterprise resource planning (ERP) systems are one of the Cloud computing research topics in particular face security challenges with cloud computing, and the paper "Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology" discusses these challenges and suggests a security mechanism and pillars for protecting ERP systems on cloud technology.

The authors begin by going over the benefits of ERP systems and cloud computing as well as the security issues with cloud computing, like data breaches and insider threats. They then go on to present a security framework for cloud-based ERP systems that is built around four pillars: access control, data encryption, data backup and recovery, and security monitoring. The access control pillar restricts user access, while the data encryption pillar secures sensitive data. Data backup and recovery involve backing up lost or failed data. Security monitoring continuously monitors the ERP system for threats. The authors also discuss interoperability challenges and the need for standardization in securing ERP systems on the cloud. They propose future research directions, such as applying machine learning and artificial intelligence to security analytics.

Overall, the paper outlines a thorough strategy for safeguarding ERP systems using cloud computing and emphasizes the significance of addressing security issues related to this technology. Organizations can protect their ERP systems and make sure the Security as well as privacy of their data by implementing these security pillars and mechanisms. 

Investigate the application of blockchain technology to enhance the security of cloud-based ERP systems. Look into the use of machine learning and artificial intelligence to identify and stop security threats in cloud-based ERP systems. Create fresh security measures that are intended only for cloud-based ERP systems. By more effectively managing access control and data encryption, cloud-based ERP systems can be made more secure. Inform ERP users about the security dangers that come with cloud-based ERP systems and how to avoid them.

10. Optimized data storage algorithm of IoT based on cloud computing in distributed system

The article proposes an optimized data storage algorithm for Internet of Things (IoT) devices which runs on cloud computing in a distributed system. In IoT apps, which normally generate huge amounts of data by various devices, the algorithm tries to increase the data storage and faster retrials of the same. 

The algorithm proposed includes three main components: Data Processing, Data Storage, and Data Retrieval. The Data Processing module preprocesses IoT device data by filtering or compressing it. The Data Storage module distributes the preprocessed data across cloud servers using partitioning and stores it in a distributed database. The Data Retrieval module efficiently retrieves stored data in response to user queries, minimizing data transmission and enhancing query efficiency. The authors evaluated the algorithm's performance using an IoT dataset and compared it to other storage and retrieval algorithms. Results show that the proposed algorithm surpasses others in terms of storage effectiveness, query response time, and network usage. 

They suggest future directions such as leveraging edge computing and blockchain technology for optimizing data storage and retrieval in IoT applications. In conclusion, the paper introduces a promising method to improve data archival and retrieval in distributed cloud based IoT applications, enhancing the effectiveness and scalability of IoT applications.

Create a data storage algorithm capable of storing and managing large amounts of IoT data efficiently. Examine the use of cloud computing to improve the performance and scalability of data storage algorithms for IoT. Create a secure and privacy-preserving data storage algorithm. Assess the performance and effectiveness of data storage algorithms for IoT in real-world applications.

How to Write a Perfect Research Paper?

  • Choose a topic: Select the topic which is interesting to you so that you can share things with the viewer seamlessly with good content. 
  • Do your research: Read books, articles, and websites on your topic. Take notes and gather evidence to support your arguments.
  • Write an outline: This will help you organize your thoughts and make sure your paper flows smoothly.
  • Start your paper: Start with an introduction that grabs the reader's attention. Then, state your thesis statement and support it with evidence from your research. Finally, write a conclusion that summarizes your main points.
  • Edit and proofread your paper. Make sure you check the grammatical errors and spelling mistakes. 

Cloud computing is a rapidly evolving area with more interesting research topics being getting traction by researchers and practitioners. Cloud providers have their research to make sure their customer data is secured and take care of their security which includes encryption algorithms, improved access control and mitigating DDoS – Deniel of Service attack etc., 

With the improvements in AI & ML, a few features developed to improve the performance, efficiency, and security of cloud computing systems. Some of the research topics in this area include developing new algorithms for resource allocation, optimizing cloud workflows, and detecting and mitigating cyberattacks.

Cloud computing is being used in industries such as healthcare, finance, and manufacturing. Some of the research topics in this area include developing new cloud-based medical imaging applications, building cloud-based financial trading platforms, and designing cloud-based manufacturing systems.

Frequently Asked Questions (FAQs)

Data security and privacy problems, vendor lock-in, complex cloud management, a lack of standardization, and the risk of service provider disruptions are all current issues in cloud computing. Because data is housed on third-party servers, data security and privacy are key considerations. Vendor lock-in makes transferring providers harder and increases reliance on a single one. Managing many cloud services complicates things. Lack of standardization causes interoperability problems and restricts workload mobility between providers. 

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are the cloud computing scenarios where industries focusing right now. 

The six major components of cloud infrastructure are compute, storage, networking, security, management and monitoring, and database. These components enable cloud-based processing and execution, data storage and retrieval, communication between components, security measures, management and monitoring of the infrastructure, and database services.  

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Vinoth Kumar P

Vinoth Kumar P is a Cloud DevOps Engineer at Amadeus Labs. He has over 7 years of experience in the IT industry, and is specialized in DevOps, GitOps, DevSecOps, MLOps, Chaos Engineering, Cloud and Cloud Native landscapes. He has published articles and blogs on recent tech trends and best practices on GitHub, Medium, and LinkedIn, and has delivered a DevSecOps 101 talk to Developers community , GitOps with Argo CD Webinar for DevOps Community. He has helped multiple enterprises with their cloud migration, cloud native design, CICD pipeline setup, and containerization journey.

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COMMENTS

  1. IEEE Cloud Computing

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  2. Cloud Computing: Possibilities, Challenges and Opportunities with

    Cloud computing is actually a model for enabling convenient, limitless, on demand network access to a shared pool of computing resource. This paper describes some aspect of cloud architecture including cloud based information mechanism, especially useful in information system. Paper mention about the challenges and opportunities of cloud based ...

  3. A Systematic Literature Review on Cloud Computing Security: Threats and

    Abstract: Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature.

  4. (PDF) Research Paper on Cloud Computing

    Research Paper on Cloud Computing June 2021 Authors: Mrs Ashwini Sheth Sachin Shankar Bhosale I.C.S.COLLEGE OF ARTS COMMERCE AND SCIENCE KHED RATANGIRI Mr Harshad Kadam Asst Prof Abstract Cloud...

  5. A Review Paper on Cloud Computing

    This paper introduces the concepts, history pros, and cons of cloud computing. Now coming to IoT, it can be any device equipment, or object which connects us with the cloud using the internet or with another device that is connected.

  6. Research Agenda in Cloud Technologies

    In this paper we discuss the advances and research questions in technical aspects of Cloud Computing, such as protocols, interoperability and techniques for building clouds, while we discuss the research challenges facing enterprise users, such as cost evaluations, legal issues, trust, privacy, security, and the effects of cloud computing on the...

  7. (PDF) A Review Paper on Cloud Computing

    According to the official definition by the National Institute of Standards and Technology, NIST: "Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a...

  8. cloud computing Latest Research Papers

    pp. 2087 Author (s): Noha G. Elnagar Ghada F. Elkabbany Amr A. Al-Awamry Mohamed B. Abdelhalim Keyword (s): Cloud Computing Load Balancing Response Time Processing Time Virtual Machines Active Monitoring Load Fluctuation Load Balancing Algorithm And Performance Load Balancer

  9. Big data analytics in Cloud computing: an overview

    Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot of variety. Big Data is a concept that deals with storing, processing and ...

  10. A Study of Cloud Computing Adoption in Universities as a Guideline to

    The transition to cloud computing in universities is an important step in terms of online education, economic crisis, globalization, and high and constantly changing requirements, especially in the COVID-19 period. Cloud computing can play a very important role in quickly solving the problems faced by universities during this coronavirus period.

  11. Cloud Computing

    romoreira/sfi2-energy-sustainability • • 18 Jul 2023. Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. 2. 18 Jul 2023. Paper.

  12. Research on Mobile Cloud Computing: Review, Trend and Perspectives

    According to the top ten strategic technology trends for 2012 [1] provided by Gartner (a famous global analytical and consulting company), cloud computing has been on the top of the list, which means cloud computing will have an increased impact on the enterprise and most organizations in 2012.

  13. (PDF) Research and Development on Cloud Computing

    Abstract. The basic principles of cloud computing is to make the computing be assigned in a great number of distributed computers, rather than local computer or remoter server. The running of the ...

  14. Cloud Computing Architecture: A Critical Analysis

    This paper examines present developments in the cloud computing architecture and presents guidance for additional research. Papers published in journals, conferences, white papers were analyzed. The objective of this present work is to identify, examine and explain the current trends and development in cloud computing architecture.

  15. PDF TOP 10 CLOUD COMPUTING PAPERS

    A principal goal of this paper is to identify privacy and confidentiality issue that may be of interest and concern to cloud computing participants and users [11]. Thus this paper explores to elicit possible issues and regulations in the area of privacy that affect the implementation of Cloud Computing Technologies.

  16. (PDF) The Future of Cloud Computing: Opportunities ...

    In this paper, we focus on different aspects of cloud computing for holistic management of cloud resources in an energy-efficient, reliable and sustainable manner. We recognized different...

  17. Top 10 Cloud Computing Research Topics in 2020

    Top 10 Cloud Computing Research Topics in 2020 - GeeksforGeeks Top 10 Cloud Computing Research Topics in 2020 Read Cloud computing has suddenly seen a spike in employment opportunities around the globe with tech giants like Amazon, Google, and Microsoft hiring people for their cloud infrastructure.

  18. Virtualization in Cloud Computing: Developments and Trends

    Abstract: Cloud computing is an interesting paradigm that is making computing and other related activities easy for consumers. The cloud infrastructure is not new, but it is working on new technology based on various services offered. The cloud provides application software online for users to conduct common activities like word processing.

  19. Top 10 Cloud Computing Research Topics of 2024

    4. Blockchain data-based cloud data integrity protection mechanism. The "Blockchain data-based cloud data integrity protection mechanism" paper suggests a method for safeguarding the integrity of cloud data and which is one of the Cloud computing research topics. In order to store and process massive amounts of data, cloud computing has grown ...

  20. (PDF) A REVIEW OF THE SECURITY ISSUES IN CLOUD COMPUTING ...

    Computing Methodologies Computer Science and Engineering Distributed Computing Cloud A REVIEW OF THE SECURITY ISSUES IN CLOUD COMPUTING AND ITS REMEDIAL ACTION INFORMATION TECHNOLOGY IN...

  21. Term Paper On Cloud Computing

    Term Paper on Cloud Computing.docx - Free download as Word Doc (.doc / .docx), PDF File (.pdf), Text File (.txt) or read online for free. This was my Term Paper on the Introduction to Cloud Computing.

  22. High-Performance Cloud Computing: A View of Scientific Applications

    The field of High Performance Computing (HPC) aims to make efficient use of the high performance of the computational power provided by supercomputers' architecture (e.g., big servers, clusters ...

  23. (PDF) Mobile Cloud Computing: A Review

    Salahaddin University - Erbil In today's world, the swift increase of utilizing mobile services and simultaneously discovering of the cloud computing services, made the Mobile Cloud...