Date of Publication :14th March 2018
Abstract: Appearance of Science Clouds empowers researchers to encourage enormous scale logical computational examinations over cloud condition. Many undertaking figuring (MTC) in computational science needs to testament stable executions of applications even in quick changes of crucial status of physical assets and supports superior assets in a long enough said. Auto-scaling approach on virtual machines (VM) increments effective cloud assets the board for the computational critical thinking condition. Different auto scaling techniques which give valuable asset the executives by and by are being discussed and examined. In any case, the majority of the auto-scaling strategies are simply effectively considered in execution measurements or execution cut-off time in explicit outstanding tasks at hand yet not in different examples of work process. We propose an auto-scaling technique, ensuring the execution of different examples of work process inside cut-off time in cross breed cloud condition. The test results show the technique works powerfully also, acceptably on half and half cloud assets for different work process designs having arbitrary remaining burden reliance.
Reference :
-
- A. F. Antonescu and T. Braun, “Simulation of SLA-based VM-scaling algorithms for clouddistributed applications,” Futur. Gener. Comput. Syst., 2016, doi: 10.1016/j.future.2015.01.015.
- C. W. Huang, W. H. Hu, C. C. Shih, B. T. Lin, and C. W. Cheng, “The improvement of autoscaling mechanism for distributed database - A case study for MongoDB,” in 15th Asia-Pacific Network Operations and Management Symposium: “Integrated Management of Network Virtualization”, APNOMS 2013, 2013.
- J. Jiang, J. Lu, G. Zhang, and G. Long, “Optimal cloud resource auto-scaling for web applications,” in Proceedings - 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, 2013, doi: 10.1109/CCGrid.2013.73.
- R. Han, L. Guo, M. M. Ghanem, and Y. Guo, “Lightweight resource scaling for cloud applications,” in Proceedings - 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012, 2012, doi: 10.1109/CCGrid.2012.52.
- K. Krampis et al., “Cloud BioLinux: Preconfigured and on-demand bioinformatics computing for the genomics community,” BMC Bioinformatics, 2012, doi: 10.1186/1471-2105- 13-42.
- L. Popa, G. Kumar, M. Chowdhury, A. Krishnamurthy, S. Ratnasamy, and I. Stoica, “FairCloud: Sharing the network in cloud computing,” in Computer Communication Review, 2012, doi: 10.1145/2377677.2377717.
- T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments,” J. Grid Comput., 2014, doi: 10.1007/s10723-014-9314-7.
- E. Caron, F. Desprez, and A. Muresan, “Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients,” J. Grid Comput., 2011, doi: 10.1007/s10723-010-9178- 4.