research paper on applications of cloud computing

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Published: 20 April 2010

Cloud computing: state-of-the-art and research challenges

Qi Zhang 1 ,
Lu Cheng 1 &
Raouf Boutaba 1

Journal of Internet Services and Applications volume 1 , pages 7–18 ( 2010 ) Cite this article

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Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

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Zhang, Q., Cheng, L. & Boutaba, R. Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1 , 7–18 (2010). https://doi.org/10.1007/s13174-010-0007-6

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research paper on applications of cloud computing

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Computer Science > Distributed, Parallel, and Cluster Computing

Title: cloudsim 7g: an integrated toolkit for modeling and simulation of future generation cloud computing environments.

Abstract: Cloud Computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large-scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate on the effectiveness of novel algorithms and mechanisms in large-scale scenarios. This article unveils CloudSim7G, the seventh generation of CloudSim, one of the first simulators specialized in evaluating resource management techniques for Cloud infrastructures. In particular, CloudSim7G features a re-engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions, which were previously available independently and often had compatibility issues, within the same simulated environment. Such architectural change is coupled with an extensive refactoring and refinement of the codebase, leading to the removal of over 13,000 lines of code without loss of functionality. As a result, CloudSim7G delivers significantly better performance in both run-time and total memory allocated (up to ~20% less heap memory allocated), along with increased flexibility, ease-of-use, and extensibility of the framework. These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next-generation cloud computing environments.

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Efficient Task Scheduling in Cloud Computing Using a Cnn-Enhanced Sine Cosine Harris Hawk Optimization Algorithm

41 Pages Posted: 29 Aug 2024

Chirag Chandrashekar

affiliation not provided to SSRN

Pradeep Krishnadoss

Vijayakumar kedalu poornachary, arun kumar sivaraman, ajmery sultana.

Algoma University

Cloud computing, a rapidly advancing technology, offers flexible payment models and high performance, accommodating vast amounts of application data and operations. Efficient management of data-transfer and the time taken for processing, are crucial for data-intensive applications. Despite numerous task scheduling algorithms proposed to handle these jobs, they often encounter issues like slow convergence and getting stuck in local minima. To tackle these challenges, this manuscript proposes a hybrid algorithm named Convolution Neural Network-based Modified Sine Cosine Harris Hawk Optimization (C-SHO). C-SHO integrates the exploration and exploitation capabilities of the Sine Cosine Algorithm (SCA) with the fast convergence rate of Harris Hawk Optimization (HHO), incorporating randomness and energy factors using a Gaussian-based concept to achieve the required ideal outcomes. Additionally, it leverages a deep learning model, CNN, to classify tasks, reducing processing time and enhancing algorithm performance. By introducing a deep learning-based CNN model for population initialization along with techniques inspired by other algorithms, C-SHO achieves superior performance compared to benchmark algorithms such as SCA, HHO, Modified-Transfer Function Based Binary Particle Swarm Optimization (MTF-BPSO), and Quantum Based Avian Navigation Optimizer Algorithm (QANA). Experimental results conducted in a simulated cloud environment using Cloudsim software display the enhanced convergence capabilities and faster rates achieved by C-SHO. Thus, by integrating the strengths of both the SCA and HHO algorithms and using a deep learning model, the C-SHO algorithm effectively allocates tasks to the appropriate virtual machines, thereby ensuring improvement in the cloud environment's overall performance.

Keywords: Cloud Computing, convergence rate, sine cosine algorithm, Harris Hawk optimization, Deep learning

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Cloud Computing

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.

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.

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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.

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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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 ].

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.

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.

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 ].

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 ].

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.

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.

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 .

BigQuery execution details

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.

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.

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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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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A Research on Genetic Algorithm-Based Task Scheduling in Cloud-Fog Computing Systems

Published: 28 August 2024
Volume 58 , pages 392–407, ( 2024 )

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Wang Hao 1 ,
Li Hui 1 , 2 ,
Song Duanzheng 1 &
Zhu Jintao 1

In recent years, the proliferating of IoT (Internet of things)-originated applications have generated huge amounts of data, which has put enormous pressure on infrastructures such as the network cloud. In this regard, scholars have proposed an architectural model for “cloud-fog” computing, where one of the obstacles to fog computing is how to allocate computing resources to minimize network resources. A heuristic-based TDCC (Time, distance, cost and computing-power) algorithm is proposed to optimize the task scheduling problem in this heterogeneous system for genetic algorithm-based “cloud-fog” computing, including execution time, operational cost, distance and total computing power resources. The algorithm uses evolutionary genetic algorithms as a research tool to combine the advantages of cloud computing, fog computing and genetic algorithms to achieve a balance between latency, cost, link length and computing power. In the hybrid computing task scheduling, this algorithm has a better balance than TCaS algorithm which only considers a single metric; this algorithm has a better adaptation value than traditional MPSO algorithm by 2.61%, BLA algorithm by 6.92% and RR algorithm by 33.39%, respectively. The algorithm is also flexible enough to match the user’s needs for high performance distance-cost-computing power, enhancing the effectiveness of the system.

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This work is supported by the National Natural Science Foundation of China (61661018), and the Research Initiation Fund Project of Binjiang College of Nanjing University of Information Science & Technology (2021r006). Li Hui is the corresponding author of this article.

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Wang Hao, Hui, L., Duanzheng, S. et al. A Research on Genetic Algorithm-Based Task Scheduling in Cloud-Fog Computing Systems. Aut. Control Comp. Sci. 58 , 392–407 (2024). https://doi.org/10.3103/S0146411624700512

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Received : 28 August 2023

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DOI : https://doi.org/10.3103/S0146411624700512

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