Energy-saving scheduling on IaaS HPC cloud environments based on a multi-objective genetic algorithm
- 65 Downloads
Nowadays, cloud computing is a growing scenario applied to many scientific and manufacturing areas due to its flexibility for adapting to highly demanding computing requirements. The advantages of pay-as-you-go model, elasticity, and the flexibility and customization offered by virtualization make cloud computing an attractive option for meeting the needs of some high-performance computing (HPC) users. However, in this environment, the inherent resources heterogeneity, the virtual machine resource sharing, and the HPC-agnostic cloud schedulers are some bottlenecks for effective HPC in cloud. Furthermore, the energy factor has added another layer of complexity in the task scheduling because of the necessity of maximizing the resources utilization and reducing their idle states. In such a complex infrastructure, the scheduling process that allocates the user parallel tasks, represented by cloudlets, to the virtual machines becomes the focus not only to reduce the job execution times, but also to deal with the energy-performance trade-off. In this work, we propose a multi-objective genetic algorithm to determine the most suitable allocation of cloudlets to the available virtual machines. This innovative approach is able to generate scheduling decisions evading systematic allocations and providing new chances for the remaining cloudlets to be scheduled in order to reduce the whole execution time and also the energy consumption. We validated our proposal using real workload traces from HPC environments and compared the results with well-known algorithms from the literature. The obtained results showed that our proposal achieves lower execution times and minimum energy consumption compared with other classic algorithms from the literature.
KeywordsCloudlet scheduling Genetic algorithm VM black-list IaaS HPC CloudSim Cloud computing
This work has been supported by the MEyC-Spain under contract TIN2014-53234-C2-2-R, TIN2016-81840-REDT, and TIN2017-84553-C2-2-R.
- 2.Gupta A, Kalé LV (2013) Towards efficient mapping, scheduling, and execution of hpc applications on platforms in cloud. In: 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and Ph.D. Forum, pp 2294–2297Google Scholar
- 6.Geetha V, Devi RA, Ilavenil T, Begum SM, Revathi S (2016) Performance comparison of cloudlet scheduling policies. In: 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp 1–7Google Scholar
- 7.Binitha S, Sathya SS et al (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137–151Google Scholar
- 8.Agarwal A, Jain S (2014) Efficient optimal algorithm of task scheduling in cloud computing environment. CoRR, abs/1404.2076Google Scholar
- 11.Singh Shekhar, Kalra Mala (2014) Task scheduling optimization of independent tasks in cloud computing using enhanced genetic algorithm. Int J Appl Innov Eng Manag 3(7):2319–4847Google Scholar
- 12.Liu Jing, Luo Xing-Guo, Zhang Xing-Ming, Zhang Fan, Li Bai-Nan (2013) Job scheduling model for cloud computing based on multi-objective genetic algorithm. IJCSI Int J Comput Sci Issues 10(1):134–139Google Scholar
- 13.Tawfeek MA, El-Sisi A, Keshk AE, Torkey FA (2013) Cloud task scheduling based on ant colony optimization. In: 2013 8th International Conference on Computer Engineering Systems (ICCES), pp 64–69Google Scholar
- 14.Bhoi U, Ramanuj PN et al (2013) Enhanced max-min task scheduling algorithm in cloud computing. Int J Appl Innov Eng Manag (IJAIEM) 2(4):259–264Google Scholar
- 15.Goyal Tarun, Agrawal Aakansha (2013) Host scheduling algorithm using genetic algorithm in cloud computing environment. Int J Res Eng Technol (IJRET) 1(1):7–12Google Scholar
- 18.Cocaña-Fernández A, Rodríguez-Soares J, Sánchez L, Ranilla J (2017) Improving the energy efficiency of virtual data centers in an it service provider through proactive fuzzy rules-based multicriteria decision making. J Supercomput 1–16Google Scholar
- 21.Feitelson D (2005) Parallel workloads archive. http://www.cs.huji.ac.il/labs/parallel/workload