STUDY ON CLOUD COMPUTING FOR EFFICIENT RESOURCE ALLOCATION AND SCHEDULING APPROACHES

Authors

  • ATHMAKURI NAVEEN KUMAR

Abstract

The term "cloud storage" refers to a variety of online services that enable users to store and share digital media such as documents, data, photos, and videos. You may access these files from anywhere and on any device (laptop, mobile phone, tablet etc). With cloud computing, users are able to allocate their computing needs among a shared pool of powerful machines, increasing their access to resources like processing power, storage space, and software services. There is a growing population of internet users who rely on cloud-based resources. Large amounts of data are transferred from users to hosts and from hosts to users in the cloud environment, but as demand for cloud services grows, the associated cost and complexity for the cloud provider may become unsustainable. There may be times when two or more users make a request for the same item. Given these constraints, it's not easy to decide which host to use to gain access to the necessary resources and build a virtual machine (VM) in which to run the necessary applications in a way that maximises efficiency while minimising costs. Scheduling tasks in a cloud computing environment to maximise efficiency is one solution to this issue. An approach to job scheduling is provided by this project. In this research, we make an effort to suggest a host selection model based on shortest execution time to reduce overhead.

Downloads

Download data is not yet available.

References

Tsai J-T, Fang J-C, Chou J-H (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055

Maguluri ST, Srikant R (2014) Scheduling jobs with unknown duration in clouds. IEEE/ACM Trans Netw (TON) 22(6):1938–1951

Cheng C, Li J, Wang Y (2015) An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Sci Technol 20(1):28–39

Lin W, Liang C, Wang JZ, Buyya R (2014) Bandwidth-aware divisible task scheduling for cloud computing. Software: Practice and Experience 44(2):163–174

Ergu D, Kou G, Peng Y, Shi Y, Shi Y (2013) The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. The Journal of Supercomputing. 64(3):835-848

Zhu X, Yang LT, Chen H, Wang J, Yin S, Liu X (2014) Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Transactions on Cloud Computing 2(2):168–180

Ghanbari S, Othman M, Leong WJ, Bakar MRA (2014) Multi-criteria based algorithm for scheduling divisible load. In: Proceedings of the first international conference on advanced data and information engineering (DaEng-2013), pp 547–554

Polverini M, Cianfrani A, Ren S, Vasilakos AV (2014) Thermal aware scheduling of batch jobs in geographically distributed data centers. IEEE Transactions on Cloud Computing 2(1):71–84

Rodriguez MA, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workows on clouds. IEEE Transactions on Cloud Computing 2(2):222–235

Keshk AE, El-Sisi AB, Tawfeek MA (2014) Cloud task scheduling for load balancing based on intelligent strategy. Int J Intell Syst Appl 6(5):25

Shamsollah G, Othman M (2012) Priority based job scheduling algorithm in cloud computing. Procedia Engineering 50:778–785

Goudarzi H, Ghasemazar M, Pedram M (2012) Sla-based optimization of power and migration cost in cloud computing. In Proceedings of the 2012 12th IEEE/ ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012) (pp. 172-179). IEEE Computer Society

Radojevic B, Zagar M (2011) Analysis of issues with load balancing algorithms in hosted (cloud) environments. In: MIPRO, 2011 proceedings of the 34th international convention, pp 416–420

Min AN, Bilal QM, Saleh A, Omer FR (2019) Cost-efcient resource allocation for real-time tasks in embedded systems. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2019.101523

Kholidy HA (2020) An intelligent swarm based prediction approach for predicting cloud computing user resource needs. Comput Commun 151:133–144. https://doi.org/10.1016/j.comcom.2019.12.028

Than MM, Thein T (2020) Energy-saving resource allocation in cloud data centers. In: IEEE conference on computer applications (ICCA), Yangon, Myanmar, pp 1–6. https://doi.org/10.1109/ ICCA49400.2020.9022819

Srimoyee B, Rituparna D, Sunirmal K, Sarbani R (2020) Energyefcient migration techniques for cloud environment: a step toward green computing. J Supercomput 76:5192–5220. https:// doi.org/10.1007/s11227-019-02801-0

Mansouri N, Javidi MM (2020) Cost-based job scheduling strategy in cloud computing environments. Distrib Parallel Databases 38:365–400. https://doi.org/10.1007/s10619-019-07273-y

Reshmi B, Poongodi P (2020) Proft and resource availabilityconstrained optimal handling of high- performance scientifc computing tasks. J Supercomput 76:4247–4261. https://doi. org/10.1007/s11227-018-2332-7

Tripathi A, Pathak I, Vidyarthi DP (2020) Modifed dragonfy algorithm for optimal virtual machine placement in cloud computing. J Netw Syst Manage. https://doi.org/10.1007/ s10922-020- 09538-9

Dorigo Marco and Gambardella Luca Maria (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66. https://doi. org/10.1109/4235.585892

Downloads

Published

2022-12-31

How to Cite

KUMAR, A. N. . (2022). STUDY ON CLOUD COMPUTING FOR EFFICIENT RESOURCE ALLOCATION AND SCHEDULING APPROACHES. The Journal of Contemporary Issues in Business and Government, 28(4), 1379–1387. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/2674