##plugins.themes.bootstrap3.article.main##

Cloud computing is a new paradigm which deliver computing resources as utility. Datacenters as cloud infrastructure encounter with several issues such as power management for the sake of economic viewpoint. Researches show that high rate of power wastage in large scale datacenters is related to sprawl resource usage making low utilization. More recently, server consolidation techniques have been developed to maximize resource utilization in at least used number of physical servers. This technique is applied in virtualization environment which allows physical servers to host several operating systems and related applications. Server consolidation approach abstracts system under study into NP-hard bin-packing problem. Several works have been done in literature to solve server consolidation problem. This paper analyses the papers and compare them with parameters derived from research context. Commonalities and differences are argued. Then, open issues and challenges are concluded to work in future.

Downloads

Download data is not yet available.

References

  1. Armbrust M, Fox A, Griffith R, D. Joseph A and Katz R, ‘‘Above the Clouds: A Berkeley View of Cloud Computing’’. Technical report EECS-2009-28, UC Berkeley, 2009.
     Google Scholar
  2. Y. Jararweh, M. Jarrah, M. kharbutli,Z. Alshara, M. N. Alsaleh, M. Al-Ayyoub: CloudExp: A comprehensive cloud computing experimental framework, Simulation Modelling Practice and Theory 49 (2014) 180–192.
     Google Scholar
  3. J. Békési, G. Galambos, H. Kellerer, A 5/4 linear time bin packing algorithm, J. Comput. System Sci. 60 (1) (2000) 145–160.
     Google Scholar
  4. A. Corradi, M. Fanelli, L. Foschini, VM consolidation: A real case based on OpenStack Cloud, Future Generation Computer systems, 32(2014) 118-127.
     Google Scholar
  5. M. Cardosa, M. Korupolu, A. Singh, Shares and utilities based power consolidation in virtualized server environments, in: Proceedings of IFIP/IEEEIntegrated Network Management (IM’09), 2009, pp. 327–334.
     Google Scholar
  6. H. Yang, Q. Zhao, Z. Luan, D. Qian, iMeter: An integrated VM power model based on performance profiling, Future Generation Computer Systems, In Press.
     Google Scholar
  7. P. Mell, T. Grance, The NIST definition of cloud computing, Natl. Inst. Stand. Technol. 53 (6) (2009) 50.
     Google Scholar
  8. F. Liu, J. Tong, J. Mao, R. Bohn, J. Messina, L. Badger, D. Leaf, NIST Cloud Computing Reference Architecture NIST Special Publication 500-292, 2011.
     Google Scholar
  9. B.P. Rimal, E. Choi, A Toxonomy and Survey of Cloud Computing Systems,Fifth International Joint Conference on INC, IMS and IDC (2009).
     Google Scholar
  10. Cloud Computing: Paradigms and Technologies ( springerlink)
     Google Scholar
  11. B. Speitkamp, M. Bichler, A mathematical programming approach for server consolidation problems in virtualized data centers, IEEE Trans. Services Comput. (2010) 266–278.
     Google Scholar
  12. Kusic, D., Kephart, J. O., Hanson, J. E., Kandasamy, N., & Jiang, G. (2009). Power and performance management of virtualized computing environments via lookahead control. Cluster Computing, 12(1), 1–15. 975.
     Google Scholar
  13. Y. Gao, H. Guan, Z. Qi, Y. Hou, L. Liu, A multi-objective ant colony system algorithm for virtual machine placement in cloud computing, Journal of Computer and System Sciences, In press.
     Google Scholar
  14. A. Verma, P. Ahuja, A. Neogi, pMapper: power and migration cost aware application placement in virtualized systems, in: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, 2008, pp. 243–264.
     Google Scholar
  15. B. Kartarciet. Al., Inter-and-Intra Data Center VM-Placement for Energy-Efficient Large-Scale Cloud Systems, First International workshop on Management and Security technologies for Cloud Computing 2012.
     Google Scholar
  16. G.Khanna, K. Beaty, G. Kar, A. Kochut, Application Performance Management in Virtualized Server Environments, Network Operations and Management Symposium , 2006. NOMS (2006).
     Google Scholar
  17. K. S. Rao, P. S. Thilagam ,Heuristics based server consolidation with residual resource defragmentation in cloud data centers, Future Generation Computer Systems. In Press.
     Google Scholar
  18. L.T. Kou, G. Markowsky, Multidimensional bin packing algorithms, IBM Journal of Research and Development 21 (5) 1977.
     Google Scholar
  19. R. Calheiros, R. Ranjan, C. De Rose, R. Buyya, Cloudsim: a novel framework for modeling and simulation of cloud computing infrastructures and services. arXiv:0903.2525.
     Google Scholar
  20. R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. De Rose, R. Buyya, Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience 41 (1) (2011) 23–50.
     Google Scholar
  21. Buyya, R., Ranjan, R., & Calheiros, R. N. (2009). Modeling and simulation of scalable cloud computing environments and the CloudSim Toolkit: Challenges and opportunities. In Proceedings of the seventh high performance computing and simulation conference (HPCS 2009, ISBN: 978-1-4244-49071), Leipzig, Germany (pp. 21–24). New York, USA: IEEE Press.
     Google Scholar
  22. W. Norcott, D. Capps, IOzone filesystem benchmark. http://www.iozone.org, 2003.
     Google Scholar
  23. P. Mucci, K. London, J. Thurman, The Cachebench Report, University of Tennessee, Knoxville, TN, 1998,
     Google Scholar
  24. IBM Active Energy Manager, http://www-03.ibm.com/systems/management/director/extensions/actengmrg.html
     Google Scholar
  25. HPL-A Portable Implementation of the High Performance Linpack Benchmark for Distributed Memory Computers, http://www.netlib.org/benchmark/hpl/
     Google Scholar
  26. OpenStack Cloud: http://www.openstack.org/, 2011.
     Google Scholar
  27. N.Kim, J.Cho and E.seo, Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud Systems, Future Generation Computing Sysytems. In Press.
     Google Scholar
  28. Murray G Patterson. What is energy efficiency?: Concepts, indicators and methodological issues. Energy Policy, 24(5):377 – 390, 1996. ISSN 0301-4215. doi: 10.1016/0301-4215(96)00017-1.
     Google Scholar
  29. M. Garey and R. Graham, “Resource Constrained Scheduling as Generalized Bin Packing,” J. Combinatorial Theory, Series A, vol. 21,pp. 257-298, Nov. 1976.
     Google Scholar
  30. T. C. Ferreto, M.A.S. Netto, R. N. Calheiros, C.A.F. De Rose, Server consolidation with migration control for virtualized data centers, Future Generation Computer Systems 27 (2011) 1027–1034.
     Google Scholar
  31. L. Jian-ping, X.Li, C. Min-rong, Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers, Expert Systems With Applications, in press.
     Google Scholar