AN OVERVIEW OF RESOURCE ALLOCATION STRATEGIES IN CLOUD COMPUTING

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Cloud computing has emerged as a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower their infrastructure costs by renting resources from the cloud for storage and other processing needs. They can also utilize pay-as-you-go applications that are accessible to the entire firm. Therefore, obtaining licenses for specific products is not necessary. Optimizing the resources that are being allocated is one of the main challenges associated with cloud computing. Resource allocation is carried out with the goal of reducing the expenses related to the model because of its uniqueness. Meeting customer requests and application requirements presents additional hurdles for resource allocation. A detailed discussion of the issues associated with different resource distribution systems is provided in this work. Both cloud users and scholars are expected to find this study useful in overcoming obstacles.

References

1. A.Singh, M.Korupolu and D.Mohapatra. Server-storage virtualization: Integration and Load balancing in data centers. In Proc.2008 ACM/IEEE conference on supercomputing (SC’08) pages 1- 12, IEEE Press 2008.

2. AndrzejKochut et al.: Desktop Workload Study with Implications for Desktop Cloud Resource Optimization, 978-1-4244-6534-7/10 2010 IEEE.

3. D. Gmach, J.RoliaandL.cherkasova, Satisfying service level objectives in a self-managing resource pool. In Proc. Third IEEE international conference on self-adaptive and self organizing system.(SASO’09) pages 243-253.IEEE Press 2009.

4. David Irwin, PrashantShenoy, Emmanuel Cecchet and Michael Zink:Resource Management in Data-Intensive Clouds: Opportunities and Challenges .This work is supported in part by NSF under grant number CNS-0834243.

5. Dongwan Shin and HakanAkkan: Domain- based virtualized resource management in cloud computing.

6. Dorian Minarolli and Bernd Freisleben: Uitlity –based Resource Allocations for virtual machines in cloud computing (IEEE, 2011), pp.410-417.

7. DusitNiyato, Zhu Kun and Ping Wang: Cooperative Virtual Machine Management for Multi-Organization Cloud Computing Environment.

8. FetahiWuhib and Rolf Stadler: Distributed monitoring and resource management for Large cloud environments (IEEE, 2011), pp.970-975.

9. HadiGoudaezi and MassoudPedram: Multidimensional SLA-based Resource Allocation for Multi-tier Cloud Computing Systems IEEE 4th International conference on Cloud computing 2011, pp.324-331.

10. HadiGoudarzi and MassoudPedram: Maximizing Profit in Cloud Computing
System Via Resource Allocation: IEEE 31st International Conference on Distributed Computing Systems Workshops 2011: pp, 1- 6.

11. Hien et al. ,’Automatic virtual resource management for service hosting platforms, cloud’09,pp 1-8.

12. Hien Nguyen et al.: SLA-aware Virtual Resource Management for Cloud Infrastructures: IEEE Ninth International Conference on Computer and Information Technology 2009, pp.357-362.

13. I.Popovici et al,”Profitable services in an uncertain world”. In proceedings of the conference on supercomputing CSC2005.

14. Jiyani et al.: Adaptive resource allocation for preemptable jobs in cloud systems (IEEE, 2010), pp.31-36.

15. Jose Orlando Melendez &shikhareshMajumdar: Matchmaking with Limited knowledge of Resources on Clouds and Grids.

16. K.H Kim et al. Power-aware provisioning of cloud resources for real time services. In international workshop on Middlleware for grids and clouds and e-science, pages 1-6, 2009.

17. Karthik Kumar et al.: Resource Allocation for real time tasks using cloud computing (IEEE, 2011), pp.

18. Keahey et al.,”sky Computing”,Intenet computing, IEEE,vol 13,no.5,pp43-51,sept-Oct2009.

19. Kuo-Chan Huang &Kuan-Po Lai: Processor Allocation policies for Reducing Resource fragmentation in Multi cluster Grid and Cloud Environments (IEEE, 2010), pp.971-976.

20. Linlin Wu, Saurabh Kumar Garg and Raj kumarBuyya: SLA –based Resource Allocation for SaaS Provides in Cloud Computing Environments (IEEE, 2011), pp.195-204.

21. P.Ruth,J.Rhee, D.Xu, R.Kennell and S.Goasguen, “Autonomic Adaptation of virtual computational environments in a multi-domain infrastructure”, IEEE International conference on Autonomic Computing, 2006,pp.5-14.

22. Patricia Takako Endo et al.:Resource allocation for distributed cloud Concept and Research challenges (IEEE,2011), pp.42-46.

23. Paul Marshall, Kate Keahey& Tim Freeman: Elastic Site (IEEE, 2010), pp.43-52.

24. PenchengXiong,Yun Chi, Shenghuo Zhu, Hyun Jin Moon, CaltonPu & Hakan Hacigumus: Intelligent Management Of Virtualized Resources for Database Systems in Cloud Environment (IEEE,2011), pp.87-98.

25. RerngvitYanggratoke, FetahiWuhib and Rolf Stadler: Gossip-based resource allocation for green computing in Large Clouds: 7th International conference on network and service management, Paris, France, 24-28 October, 2011.

26. Richard T.B.Ma,Dah Ming Chiu and John C.S.Lui, Vishal Misra and Dan Rubenstein:On Resource Management for Cloud users :a Generalized Kelly Mechanism Approach.

27. ShikhareshMajumdar: Resource Management on cloud: Handling uncertainties in Parameters and Policies (CSI communicatons, 2011,edn)pp.16-19.

28. Shuo Liu Gang Quan ShangpingRen On –Line scheduling of real time services for cloud computing. In world congress on services, pages 459- 464, 2010.

29. T.Wood et al. Black Box and gray box strategies for virtual machine migration. In Proc 4th USENIX Symposium on Networked Systems Design and Implementation (NSDI 07), pages 229-242.

30. Tram Truong Huu& John Montagnat: Virtual Resource Allocations distribution on a cloud infrastructure (IEEE, 2010), pp.612-617.

31. WaheedIqbal, Matthew N.Dailey, Imran Ali and Paul Janecek& David Carrera: Adaptive Resource Allocation for Back-end Mashup Applications on a heterogeneous private cloud.

32. Weisong Hu et al.: Multiple Job Optimization in MapReduce for Heterogeneous Workloads: IEEE Sixth International Conference on Semantics, Knowledge and Grids 2010,pp. 135-140.

33. Wei-Tek Tsai Qihong Shao Xin Sun Elston, J. Service-oriented cloud computing. In world congress on services, pages 473-478, 2010.

34. Wei-Yu Lin et al.: Dynamic Auction Mechanism for Cloud Resource Allocation: 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing, pp.591-592.

35. X.Zhu et al. Integrated capacity and workload management for the next generation data center. In proc.5th international conference on Automatic computing (ICAC’08), pages 172-181,IEEE Press 2008.

36. XiaoyiLu,Jian Lin, Li Zha and ZhiweiXu: Vega Ling Cloud: A Resource Single Leasing Point System to Support Heterogenous Application Modes on Shared Infrastructure(IEEE,2011),pp.99-106.

37. Xiaoying Wang et al.: Design and Implementation Of Adaptive Resource Co-allocation Approaches for Cloud Service Environments: IEEE 3rd International Conference on Advanced Computer Theory and Engineering 2010,V2,pp,484-488.

38. Y.C Lee et.al,”Project driven service request scheduling in clouds”. In proceedings of the international symposium on cluster & Grid Computing. (CC Grid 2010), Melbourne, Australia.

39. Yang wt.al A profile based approach to Just in time scalability for cloud applications, IEEE international conference on cloud computing ,2009,pp 9-16.

40. Zhen Kong et.al: Mechanism Design for Stochastic Virtual Resource Allocation in Non-Cooperative Cloud Systems: 2011 IEEE 4th International Conference on Cloud Computing:pp, 614-621.

Keywords

Cloud Computing; Cloud Services; Resource Allocation; Infrastructure.