ABSTRACT
High performance computing (HPC) clouds consume a lot of energy (kWh); therefore reducing energy consumption is a high priority for any cloud provider. This paper studies the applications of vector bin packing heuristic and Neural Network (NN) to allocate virtual machines (VMs) onto physical machines (PMs) that minimizes total energy consumption of the physical machines. In our scenario, a list of virtual machines from request queue needs to assign to system for every interval time (T) and minimize total energy consumption. We proposed Best Fit Decreasing Neural Network (BFD-NN), which contains an evaluation function (f) that finds the most efficient physical machine for each VM from requests in system. We also proposed a process to optimize weight of coefficients in f for every request which users submit to the system based on information of users' requests in the past by using Parallel Genetic Algorithm (PGA) and Neural Network. Our method is a new approach because it not only uses knowledge from requests of users but also considers time dimension of virtual machines. Two job parallel workload models in Parallel Workloads Archive are used to evaluate our approach. The simulation results illustrate that BFD-NN could reduce up to 15% total energy consumption compared with state-of-the-art heuristics (such as Best Fit and First Fit Decreasing) in online allocation virtual machines.
- Standard Performance Evaluation Corporation. SPECpower ssj2008. http://www.spec.org/power_ssj2008/results/res2011q2/power_ssj2008-20110406-00368.html. Accessed: 19-July-2016.Google Scholar
- S. Albers. Energy-efficient algorithms. Communications of the ACM, 53(5):86--96, 2010. Google ScholarDigital Library
- L. A. Barroso, J. Clidaras, and U. Hölzle. The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis lectures on computer architecture, 8(3):1--154, 2013. Google ScholarDigital Library
- A. Beloglazov, J. Abawajy, and R. Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5):755--768, 2012. Google ScholarDigital Library
- A. Beloglazov and R. Buyya. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13):1397--1420, 2012. Google ScholarDigital Library
- L. Chen and H. Shen. Consolidating complementary vms with spatial/temporal-awareness in cloud datacenters. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pages 1033--1041. IEEE, 2014.Google ScholarCross Ref
- X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, volume 35, pages 13--23. ACM, 2007. Google ScholarDigital Library
- S. K. Garg, C. S. Yeo, A. Anandasivam, and R. Buyya. Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments. arXiv preprint arXiv:0909.1146, abs/0909.1(September 2009), 2009.Google Scholar
- I. Goiri, F. Julia, R. Nou, J. L. Berral, J. Guitart, and J. Torres. Energy-aware scheduling in virtualized datacenters. In 2010 IEEE International Conference on Cluster Computing, pages 58--67. IEEE, 2010. Google ScholarDigital Library
- T. Knauth and C. Fetzer. Energy-aware scheduling for infrastructure clouds. In Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, pages 58--65. IEEE, 2012. Google ScholarDigital Library
- J. Kolodziej, S. U. Khan, and F. Xhafa. Genetic algorithms for energy-aware scheduling in computational grids. In P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2011 International Conference on, pages 17--24. IEEE, 2011. Google ScholarDigital Library
- M. Y. Kovalyov, C. Ng, and T. E. Cheng. Fixed interval scheduling: Models, applications, computational complexity and algorithms. European Journal of Operational Research, 178(2):331--342, 2007.Google ScholarCross Ref
- K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, and T. D. Nguyen. Reducing electricity cost through virtual machine placement in high performance computing clouds. 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pages 1--12, 2011. Google ScholarDigital Library
- H. Mühlenbein, M. Schomisch, and J. Born. The parallel genetic algorithm as function optimizer. Parallel computing, 17(6--7):619--632, 1991. Google ScholarDigital Library
- R. Panigrahy, K. Talwar, L. Uyeda, and U. Wieder. Heuristics for Vector Bin Packing. Research.Microsoft.Com, 2011.Google Scholar
- Parallel Workload Models. Downey Model. http://www.cs.huji.ac.il/labs/parallel/workload/models.html#downey97. Accessed: 19-July-2016.Google Scholar
- Parallel Workload Models. Feitelson Model. http://www.cs.huji.ac.il/labs/parallel/workload/models.html#feitelson96. Accessed: 19-July-2016.Google Scholar
- N. Quang-Hung, D.-K. Le, N. Thoai, and N. T. Son. Heuristics for energy-aware vm allocation in hpc clouds. In T. K. Dang, R. Wagner, E. Neuhold, M. Takizawa, J. Küng, and N. Thoai, editors, Future Data and Security Engineering: First International Conference, FDSE 2014, Ho Chi Minh City, Vietnam, November 19--21, 2014, Proceedings, pages 248--261. Springer International Publishing, 2014.Google ScholarCross Ref
- N. Quang-Hung, P. D. Nien, N. H. Nam, N. Huynh Tuong, and N. Thoai. A genetic algorithm for power-aware virtual machine allocation in private cloud. In K. Mustofa, E. J. Neuhold, A. M. Tjoa, E. Weippl, and I. You, editors, Information and Communication Technology: International Conference, ICT-EurAsia 2013, Yogyakarta, Indonesia, March 25--29, 2013. Proceedings, pages 183--191, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg. Google ScholarDigital Library
- N. Quang-Hung and N. Thoai. Minimizing Total Busy Time for Energy-Aware Virtual Machine Allocation Problems. Proceedings of the Sixth International Symposium on Information and Communication Technology - SoICT 2015, pages 179--186, 2015. Google ScholarDigital Library
- I. Takouna, W. Dawoud, and C. Meinel. Energy efficient scheduling of hpc-jobs on virtualize clusters using host and vm dynamic configuration. ACM SIGOPS Operating Systems Review, 46(2):19--27, 2012. Google ScholarDigital Library
- R. Tanese. Parallel genetic algorithm for a hypercube. In Genetic algorithms and their applications: proceedings of the second International Conference on Genetic Algorithms: July 28--31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates, 1987., 1987. Google ScholarDigital Library
- H. Viswanathan, E. K. Lee, I. Rodero, D. Pompili, M. Parashar, and M. Gamell. Energy-aware application-centric VM allocation for HPC workloads. IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, pages 890--897, 2011. Google ScholarDigital Library
- G. von Laszewski, L. Wang, A. J. Younge, and X. He. Power-aware scheduling of virtual machines in dvfs-enabled clusters. In 2009 IEEE International Conference on Cluster Computing and Workshops, pages 1--10. IEEE, 2009.Google ScholarCross Ref
Index Terms
BFD-NN: best fit decreasing-neural network for online energy-aware virtual machine allocation problems
Recommendations
Minimizing Total Busy Time for Energy-Aware Virtual Machine Allocation Problems
SoICT '15: Proceedings of the 6th International Symposium on Information and Communication TechnologyThis paper investigates the energy-aware virtual machine (VM) allocation problems in clouds along characteristics: multiple resources, fixed interval time and non-preemption of virtual machines. Many previous works have been proposed to use a minimum ...
Customization of virtual machine allocation policy using k-means clustering algorithm to minimize power consumption in data centers
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud ComputingCloud Computing provides rapid provision of computing resources like processing power, memory, network resources, storage, etc. Running computing resources for longer time, leads energy consumption, increase the emission of Carbon Dioxide (CO2) and ...
Energy Efficient Resource Management in Virtualized Cloud Data Centers
CCGRID '10: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid ComputingRapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. We propose an energy efficient resource management system ...
Comments