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The Problem of our research work is as follows:



Scheduling was an important tool for managing various jobs and distributing the effort throughout a procedure or process in the early stages of the company. It’s been a longstanding practice to use scheduling to allocate resources, such as assigning internal/external hardware/software assets, generating procedures, purchasing materials, and so on. It used to be the responsibility of the administration to allocate the tasks and responsibilities among the workers [1].

A computer’s resources include a display, CPU (Central Processing Unit), networking devices, primary and auxiliary storage devices, printer-cum-scanner, trackpad, and many more. An operating system must have a scheduler that may define the pre-arrangement of resources in case some specific situation or process requires it to manage these resources.

Cloud computing systems are on a path to significant economic success because they can provide a vast array of services and resources to their consumers. In addition, intelligently built recommendation systems have played a significant role in helping customers determine whether or not a specific service is necessary for them. Scheduling is one of the most popular ways for allocating resources to user-defined demands in this era of cutting-edge technology. Hardware resources like expansion cards and network links can run virtual computing entities like processes or threads. An infinite amount of resources are available in the cloud, and scheduling approaches play a critical role in taking full advantage of them. Resources must be intelligently automated so that requests may be processed efficiently. If you’re looking to implement automation, an algorithm is the most important component in ensuring that jobs are distributed among multiple resources while maintaining data security.

The deployment of different services will be aided by integrating multiple heterogeneous technologies into cloud computing. Platform as a Service gives a development environment to the user without taking care of the hardware being used. In contrast, Software as a Service provides on-demand access to software available online. Using Infrastructure as a Service (IaaS) provides a simple and easy-to-use platform for deploying various services. Server utilization in IaaS can be improved by following industry standards for scheduling. IaaS providers also guarantee that surplus services may be economically deployed, whereas clients do not have to worry about service execution using numerous resources.

Virtualized and scalable resources interact dynamically with one other in a cloud service provider (CSP) like iCloud, Amazon Web Services (AWS), IBM (International Business Machines) Corporation cloud, etc. Computing clusters, where resources are transmitted over remote data centers, are outlined as cloud services. It is possible to access CSP services through a variety of different software. In addition, these resources must be appropriately managed to be utilized to the utmost extent while meeting the bare minimum of needs. Implementing an effective and efficient scheduling system is necessary to manage the demands appropriately.


Scheduling is a critical component of management in practically every industry, regardless of private or public. Items are often arranged according to specific criteria and circumstances before processing. By arranging tasks and processes logically, scheduling helps speed up the completion of a task or process. Clients can rent nearly any service they need in the cloud computing era. Individuals, a group of people, or organizations may all be referred to as “users” in this context.

Time and cost are user-defined limitations applied during service execution, whereas the service provider imposes resource utilization. Maximum satisfaction is achieved through an efficient scheduling technique that benefits both the user and the service provider. The resources must be distributed to users at the lowest possible cost and with the shortest possible completion time, while the provider’s resource utilization rate must be maximized. Cloud computing must ensure that all apps are entertained at all times [37].

An efficient scheduling technique must take into account application needs and resource status. Managing the variability of cloud resources and tasks is a critical challenge in a dispersed system. Many characteristics such as fairness, makespan, load balancing, and optimization deadline are considered for task scheduling. They are enhancing the scheduling strategy through the use of these parameters results in improved performance and optimum results.

Task assignment in distributed systems is an NP-hard issue [38–41]. These problems have as their objective the minimization of communication costs and maximization of resource usage rates. Scheduling approaches often employ heuristics and optimization techniques to arrive at a close-to-optimal solution [42].


This thesis contributes to research in the following ways, based on the numerous scheduling methodologies examined:

  • A comprehensive overview of the history, benefits, and kinds of cloud computing and virtualization.
  • An overview and analysis of contemporary cloud computing scheduling techniques.
  • A scheduling approach that is cost-effective in a cloud computing setting.
  • A scheduling method focused on deadlines is used to optimize resources in a cloud environment.
  • A novel strategy for balancing server load and maximizing resource utilization in a cloud setting.
  • Energy-aware adaptive strategy to reduce energy consumption and SLA violations.


The Thesis structure is graphically represented as follows:


Figure 1 Organization of the thesis

  • The research is mostly concerned with task scheduling. Chapter 2 presents a taxonomy and extensive analysis of the different job scheduling algorithms. This chapter teaches readers how to classify task scheduling and the different factors used in a cloud environment.
  • Chapter 3 details the study technique and tools used to conduct experimental analyses during the investigation.
  • Chapter 4 conducts an experimental comparison of known fundamental scheduling algorithms in a cloud computing setting. The chapter concludes that Min-Min executes more quickly in the cloud. However, an efficient approach is not acceptable if it ignores the cost issue.
  • Activity Depending Cost Scheduling is a scheduling strategy that determines the cost based on the nature of the job or activity. Chapter 5 discusses the cost-effective scheduling strategy in prioritizing jobs and then cost.
  • Users may also submit time-sensitive requests. Chapter 6 suggests a solution for addressing these requirements by using deadline-based work scheduling.
  • In Chapter 7, a hybrid approach to load balancing takes maximum resource use into account to reduce resource wastage. This strategy optimizes the rate at which resources are utilized in a cloud environment.
  • Chapter 8 proposes an adaptive energy-efficient technique for minimizing VM movement and minimizing SLA violation in cloud computing. VM placement considerations have been made with an eye toward energy conservation.
  • Chapter 9 summarises the entire research project and suggests some potential research directions.


[1]Wikipedia, “ Scheduling in Production,” 2013 (accessed in 2019). Available at https://en.wikipedia.org/wiki/Scheduling_(production_processes).

[2] C. Donnelly, “ A History of Cloud Computing,” April 2018 (accessed in 2019). Available at https://www.computerweekly.com/feature/ A-history-of-cloud-computing.

[3] S Garfinkel, “The Cloud Imperative,” 2011 (accessed in 2019). Available at https://www.technologyreview.com/s/425623/the-cloud-imperative/.

[4] A. Incorporated, “History of Grid Computing,” (accessed in 2019). Available at http://www.avarsys.com/grid_computing_history.html.

[5] R. K. Chellappa, “ Goizueta Business School,” (accessed in 2019). Available at https://goizueta.emory.edu/faculty/profiles/ramnath-k-chellappa.

[6]A. W. Services, “ AWS Documentation,” (accessed in 2019). Available at https://docs.aws.amazon.com/index.html?nc2=h_ql_doc

[7] Wikipedia, “Google Code-in,” (accessed in 2019). Available at https://en. wikipedia.org/wiki/Google_Code-in.

[8] Microsoft, “ What is Azure,” (accessed in 2019). Available at https://azure. microsoft.com/en-in/overview/what-is-azure.

[9]L. Peng, “Cloud computing,” Publishing House of Electronics Industry, 2010.

[10]I. Foster, Y. Zhao, I. Raicu, and S. Lu, “ Cloud Computing and Grid Computing 360-Degree Compared,” Grid Computing Environments Workshop, vol. 5, Jan. 2009.

[11]M. Naghshineh, R. Ratnaparkhi, D. Dillenberger, J. R. Doran, C. Dorai, L. Anderson, G. Pacifici, J. L. Snowdon, A. Azagury, M. VanderWiele, and Y. Wolfsthal, “ IBM Research Division Cloud Computing Initiative,” IBM Journal of Research and Development, vol. 53, pp. 1–10, July 2009.

[12] R. L. Grossman, “The Case for Cloud Computing,” IT Professional, vol. 11, pp. 23–27, March 2009.

[13] P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” tech. rep., National Institute of Standards and Technology, Sept. 2011.

[14] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, and M. Zaharia, “Above the clouds: A Berkeley view of cloud computing,” tech. Rep., Feb. 2009.

[15] Gartner, “ IT Glossary,” (accessed in 2019). Available at https://www. gartner.com/it-glossary/cloud-computing.

[16] G. Perry, “ The Open Cloud Manifesto: A Call to Action for the Worldwide Cloud Community,” March 2009 (accessed in 2019).

[17] T. Ride, “ Cloud Computing Architecture,” (accessed in 2019). Available at https://www.tutorialride.com/cloud-computing/ cloud-computing-architecture.htm.

[18]R. Buyya, J. Broberg, and A. Goscinski, Cloud Computing Principles and Paradigms. John Wiley & Sons, 2011.

[19] Wikipedia, “Utility computing,” (accessed in 2019). Available at https://en. wikipedia.org/wiki/Utility_computing.

[20] M. Rouse, “ Cloud Automation,” Aug. 2017. Available at HTTPS:// searchcloudcomputing.techtarget.com/definition/cloud-automation.






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