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SLA-based Virtual Machine Management for Mixed Workloads of Interactive Jobs in a Cloud Datacenter

by Vivek H. Bharad, Hitesh A. Bheda
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 16
Year of Publication: 2015
Authors: Vivek H. Bharad, Hitesh A. Bheda
10.5120/19747-1174

Vivek H. Bharad, Hitesh A. Bheda . SLA-based Virtual Machine Management for Mixed Workloads of Interactive Jobs in a Cloud Datacenter. International Journal of Computer Applications. 112, 16 ( February 2015), 1-3. DOI=10.5120/19747-1174

@article{ 10.5120/19747-1174,
author = { Vivek H. Bharad, Hitesh A. Bheda },
title = { SLA-based Virtual Machine Management for Mixed Workloads of Interactive Jobs in a Cloud Datacenter },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 16 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number16/19747-1174/ },
doi = { 10.5120/19747-1174 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:37.193632+05:30
%A Vivek H. Bharad
%A Hitesh A. Bheda
%T SLA-based Virtual Machine Management for Mixed Workloads of Interactive Jobs in a Cloud Datacenter
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 16
%P 1-3
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effcient provisioning of resources is a challenging problem in cloud computing environments due to its dynamic nature and the need for supporting heterogeneous applications. Even though VM (Virtual Machine) technology allows several workloads to run concurrently and to use a shared infrastructure, still it does not guarantee application performance. Thus, currently cloud datacenter providers either do not offer any performance guarantee or prefer static VM allocation over dynamic, which leads to inef?cient utilization of resources. Also, the workload may have different QoS (Quality Of Service) requirements due to the execution of various types of applications such as HPC and web, which makes resource provisioning much harder. Earlier work either concentrate on single type of SLAs (Service Level Agreements) or resource usage patterns of applications, such as web applications, leading to inef?cient utilization of datacenter resources. In this paper, we tackle the resource allocation problem within a datacenter that runs different types of application workloads, particularly non-interactive and transactional applications. We propose an admission control and scheduling mechanism which not only maximizes the resource utilization and pro?t, but also ensures that the QoS requirements of users are met as speci?ed in SLAs. In our study, we find that it is important to take care of various types of SLAs along with applicable penalties and the mix of workloads for better resource allocation and utilization of datacenters. The proposed mechanism provides substantial improvement over static server consolidation and reduces SLA violations.

References
  1. Antonescu A-F, Robinson P, Braun T. Dynamic sla management with forecasting using multi-objective optimization. In: Proceeding of 2013 IFIP/IEEE interna- tional symposium on integrated network management (IM 2013). Ghent, Belgium; 2013.
  2. Buyya R, Yeo C, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms: vision, hype and reality for delivering computing as the 5th utility. Future Generat Comput Syst 2009;25(6):599–616.
  3. Ostermann S, Iosup A, Yigitbasi N, Prodan R, Fahringer T, Epema D. An early performance analysis of cloud computing services for scienti?c computing. Delft University of Technology, PDS-2008-006.
  4. Yeo C, Buyya R. Service level agreement based allocation of cluster resources: handling penalty to enhance utility. In: Proceedings of the 7th IEEE interna- tional conference on cluster computing. Boston, USA; 2005.
  5. Nathuji R, Kansal A, Ghaffarkhah A. Q-clouds: managing performance interference effects for qos-aware clouds. In: Proceedings of the 5th European conference on Computer systems (EuroSys 2010). Paris, France; 2010.
  6. Goiri I, Julià F, Fitó JO, Macías M, Guitart J. Resource-level QOS metric for CPU-based guarantees in cloud providers. In: Proceedings of 7th international workshop on economics of grids, clouds, systems, and services. Naples, Italy; 2010.
  7. Quiroz A, Kim H, Parashar M, Gnanasambandam N, Sharma N. Towards autonomic workload provisioning for enterprise grids and clouds. In: Proceedings of 10th IEEE/ACM international conference on grid computing. Melbourne, Australia; 2009.
  8. Sotomayor B, Keahey K, Foster IT. Combining batch execution and leasing using virtual machines. In: Proceedings of the 17th international ACM symposium on high-performance parallel and distributed computing. Boston, USA; 2008.
  9. Carrera D, Steinder M, Whalley I, Torres J, Ayguadé E. Enabling resource sharing between transactional and batch workloads using dynamic application place- ment. In: Proceedings of the ACM/IFIP/USENIX 9th international middleware conference, Leuven, Belgium; 2008.
  10. Smith M, Schmidt M, Fallenbeck N, Doernemann T, Schridde C, Freisleben B. Secure on-demand grid computing. Future Gener Comput Syst 2009;25(3):315–25.
  11. Barroso L, Holzle U. The case for energy-proportional computing. Computer 2007;40(12):33–7.
  12. Kim J-K, Siegel HJ, Maciejewski AA, Eigenmann R. Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans Parallel Distrib Syst 2008;19(11):1445–57.
  13. Kim J, Ruggiero M, Atienza D, Lederberger M. Correlation-aware virtual machine allocation for energy-ef?cient datacenters. In: Proceedings of the conference on design, automation and test in Europe. Ghent, Belgium; 2013.
  14. Meng X, Isci C, Kephart J, Zhang L, Bouillet E, Pendarakis D. Ef?cient resource provisioning in compute clouds via VM multiplexing. In: Proceedings of the 7th international conference on autonomic computing, Washington, USA; 2010.
  15. Zhang W, Qian H, Wills C, Rabinovich M. Agile resource management in a virtualized data center. In: Proceedings of Ist joint WOSP/SIPEW international conference on performance engineering. California, USA; 2010.
  16. Soundararajan V, Anderson J. The impact of MNGT. Operations on the virtualized datacenter. In: Proceedings of the 37th annual international symposium on computer architecture. France; 2010.
  17. Wang Z, Zhu X, Padala P, Singhal S. Capacity and performance overhead in dynamic resource allocation to virtual containers. In: Proceedings of the 10th IFIP/IEEE international symposium on integrated network management. Munich, Germany; 2007.
  18. Minarolli D, Freisleben B. Distributed resource allocation to virtual machines via arti?cial neural networks. In: Proceedings of 22nd Euromicro international conference on parallel, distributed and network-based processing (PDP), Turin, Italy; 2014.
  19. Casalicchio E, Menascé DA, Aldhalaan A. Autonomic resource provisioning in cloud systems with availability goals. In: Proceedings of the 2013 ACM cloud and autonomic computing conference, Miami, FL, USA; 2013.
  20. Hu Y, Wong J, Iszlai G, Litoiu M. Resource provisioning for cloud computing. In: CASCON '09: Proceedings of the 2009 conference of the Center for Advanced Studies on Collaborative Research, Ontario, Canada; 2009.
Index Terms

Computer Science
Information Sciences

Keywords

SLA VM HPC (High Performance Computing)

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