The week's pick
Reseach Article
Virtual Machine Consolidation Method with Low Delay and Energy Consumption Parameters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 8 |
| Year of Publication: 2019 |
| Authors: Nayana D. Mydhili K. Nair |
10.5120/ijca2019919523
|
Nayana D. Mydhili K. Nair . Virtual Machine Consolidation Method with Low Delay and Energy Consumption Parameters. International Journal of Computer Applications. 177, 8 ( Oct 2019), 40-46. DOI=10.5120/ijca2019919523
Abstract
There is huge amount of information available and maintaining such a data will require lot of computational power and the solution for these issues is to provide a system with more flexible, reliable and scalable characters. Cloud based orchestration services support the users any time and also can be scaled based on the demand for resources. Demand and delay are directly proportional to each other and if the duration of the delay is exponential then the cost also is also exponential. Performing a balance act between the quality of service and energy consumption rate is proposed by the algorithm. Each of the data center used in the cloud have set of virtual machines. In order have better processing of data consolidation of virtual machines is done. The proposed method is built on a fuzzy logic with consolidation framework. The method filters the virtual machine from an overloaded host and the migration control mechanism build into the system helps in increasing the performance for the selection process. Three characteristics namely mean, standard deviation and media are taken to compute the overload ratio due the selection process to improve optimization.
References
- Belady C (2011) Projecting annual new datacenter construction market size. Technical Report. Microsoft Corp., US
- Zhan ZH, Liu XF, Gong YJ, Zhang J, Chung HSH, Li Y (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys (CSUR) 47(4):63
- Ismaeel,Karim,Miri ,'Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centers’, Journal of Cloud Computing,Vol-7,2018
- Prevost J, Nagothu K, Jamshidi M, Kelley B (2014) Optimal calculation overhead for energy efficient cloud workload prediction. In: World Automation Congress (WAC), 2014, pp 741–747.
- Uddin M, Darabidarabkhani Y, Shah A, Memon J (2015) Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review. Renewable and Sustainable Energy Reviews 51:1553–1563
- Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2015) Exploiting task elasticity and price heterogeneity for maximizing cloud computing profits.IEEE Transactions on Emerging Topics in Computing PP(99):1
- Fu X, Zhou C (2015) Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Frontiers of Computer Science 9(2):322–330
- Kong F, Liu X (2014) A survey on green-energy-aware power management for datacenters. ACM Computing Surveys (CSUR) 47(2):30
- Breitgand D, Da Silva DM, Epstein A, Glikson A, Hines MR, Ryu KD, Silva MA (2018) Dynamic virtual machine resizing in a cloud computing infrastructure. US Patent 9,858,095
- Gong Z, Gu X, Wilkes J (2010) Press: Predictive elastic resource scaling for cloud systems. In: 2010 International Conference on Network and Service Management, pp 9–16
- Larumbe F, Sansò B (2017) Elastic, on-line and network aware virtual machine placement within a data center. In: Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on. IEEE, pp 28–36
- Gong Z, Gu X, Wilkes J (2010) Press: Predictive elastic resource scaling for cloud systems. In: 2010 International Conference on Network and Service Management, pp 9–16
- Larumbe F, Sansò B (2017) Elastic, on-line and network aware virtual machine placement within a data center. In: Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on. IEEE, pp 28–36
- Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H. Using ant colony system to consolidate vms for green cloud computing
- Liu XF, Zhan ZH, Du KJ, Chen WN (2014) Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In: Proceedings of the 2014 conference on Genetic and evolutionary computation. ACM, pp 41–48
- H. Qaiser and G. Shu, "Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters," 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 2018, pp. 832-839.
- Y. Tao and S. Yu, "kFHCO: Optimal VM Consolidation via k -Factor Horizontal Checkpoint Oversubscription," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 380-384.
- M. A. Khan, A. P. Paplinski, A. M. Khan, M. Murshed and R. Buyya, "Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers," 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), Barcelona, 2018, pp. 105-114.
- X. Xiao, W. Zheng, Y. Xia, X. Sun, Q. Peng and Y. Guo, "A Workload-Aware VM Consolidation Method Based on Coalitional Game for Energy-Saving in Cloud," in IEEE Access, vol. 7, pp. 80421-80430, 2019.
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