International Journal of Distributed and Cloud Computing

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Energy Efficient Task Scheduling in Cloud Data Center


Affiliations
1 Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
2 Assistant Professor, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
     

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Cloud computing is emerging as a necessary need for the IT industry in order to reduce the setup and operational cost of its infrastructure. There is a huge requirement of computing resources to satisfy customer demands. A minute delay in a service may result in a measurable amount of loss for an organization. Response time is a major metric for evaluating performance of cloud applications. Cloud data centers form backbone of cloud computing. Data centers consume enormous amount of energy. Server racks have processing units, storage and network interface. Energy is dissipated at the server racks and cooling units. Various task scheduling algorithms and virtual machine scheduling algorithms have been proposed to measure the loss in performance but the energy loss is kept at the lowest priority. The paper is focused on discussing about the two techniques that maintain a scheduled routine for tasks arriving in a data center through a simulation scenario. VM-specific scheduling of tasks is done for assignment of the tasks to single or multiple virtual machines. Comparison of the two techniques, time-shared and space-shared technique is also done to give the reader a clear view about the situation in which both techniques are used. Future work is also discussed in the same context.

Keywords

Cloud Computing, Cloudlet, Energy, Space-Shared, Time-Shared.
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  • J. Baliga, R. W. A. Ayre, K. Hinton, and R. S. Tucker, “Green cloud computing: Balancing energy in processing, storage, and transport,” Proceedings of the IEEE, vol. 99, no. 1, pp. 149-167, 2011.

  • D. S. Markovic, D. Zivkovic, I. Branovic, R. Popovic, and D. Cvetkovic, “Smart power grid and cloud computing,” Renewable and Sustainable Energy Reviews, vol. 24, pp. 566-577, 2013.

  • N. J. Kansal, and I. Chana, “Cloud load balancing techniques: A step towards green computing,” IJCSI International Journal of Computer Science, vol. 9, no. 1, pp. 238-246, 2012.

  • R. Mata-Toledo, and P. Gupta, “Green data center: How green can we perform?,” Journal of Technology Research, 2011.

  • A. Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and survey of energy-efficient data centers and cloud computing systems,” Advances in Computers, vol. 82, pp. 47-111, 2011.

  • A. Banerjee, P. Agrawal, and N. Ch. S. N. Iyengar, “Energy efficiency model for cloud computing,” International Journal of Energy, Information and Communications, vol. 4, no. 6, pp. 29-42, 2013.

  • D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, “Energy-efficient data replication in cloud computing datacenters,” Cluster Computing, vol. 18, no. 1, pp. 385-402, 2015.

  • X. Dong, T. El-Gorashi, and J. M. H. Elmirghani, “Green IP over WDM networks with data centers,” Journal of Lightwave Technology, vol. 29, no. 12, pp. 1861-1880, 2011.

  • M. Lin, A. Wierman, L. L. H. Andrew, and E. Thereska, “Dynamic right-sizing for power-proportional data centers,” IEEE/ACM Transactions on Networking, vol. 21, no. 5, pp. 1378-1391, 2013.

  • S. Ghemawat, H. Gobioff, and S. T. Leung, “The Google file system,” ACM SIGOPS Operating Systems Review, vol. 37, no. 5, p. 29, 2003.

  • B. A. Milani, and N. J. Navimipour, “A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions,” Journal of Network and Computer Applications, vol. 64, pp. 229-238, 2016.

  • Y. Qu, and N. Xiong, “RFH: A resilient, fault-tolerant and high-efficient replication algorithm for distributed cloud storage,” In 2012 41st International Conference on Parallel Processing (ICPP), IEEE, 2012.

  • X. Bai, H. Jin, X. Liao, and Z. Shao, “RTRM: A response time-based replica management strategy for cloud storage system,” International Conference on Grid and Pervasive Computing, Springer, Berlin, Heidelberg, 2013.

  • D. W. Sun, G. R. Chang, S. Gao, L. Z. Jin, and X. W. Wang, “Modeling a dynamic data replication strategy to increase system availability in cloud computing environments,” Journal of Computer Science and Technology, vol. 27, no. 2, pp. 256-272, 2012.

  • T. Hamrouni, S. Slimani, and F. B. Charrada, “A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids,” Engineering Applications of Artificial Intelligence, vol. 48, pp. 140-158, 2016.

  • S. Srikantaiah, A. Kansal, and F. Zhao, “Energy-aware consolidation for cloud computing,” Proceedings of the 2008 Conference on Power Aware Computing and Systems, vol. 12, no. 1, p. 10, 2008.

  • A. Beloglazov, and R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010.

  • M. Cardosa, M. R. Korupolu, and A. Singh, “Shares and utilities based power consolidation in virtualized server environments,” 2009 IFIP/IEEE International Symposium on Integrated Network Management, IEEE, 2009.

  • 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, vol. 28, no. 5, pp. 755-768, 2012.

  • R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu, “No power struggles: Coordinated multi-level power management for the data center,” ACM SIGARCH Computer Architecture News, ACM, vol. 36, no. 1, 2008.

  • J. Shuja, K. Bilal, S. A. Madani, M. Othman, R. Ranjan, P. Balaji, and S. U. Khan, “Survey of techniques and architectures for designing energy-efficient data centers,” IEEE Systems Journal, vol. 10, no. 2, pp. 507-519, 2016.

  • N. Liu, Z. Dong, and R. Rojas-Cessa, “Task scheduling and server provisioning for energy-efficient cloud-computing data centers,” 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE, 2013.

  • N. Liu, Z. Dong, and R. Rojas-Cessa, “Task and server assignment for reduction of energy consumption in data centers,” 2012 11th IEEE International Symposium on Network Computing and Applications (NCA), IEEE, 2012.

  • D. Bouley, “Estimating a data centers electrical carbon footprint,” Schneider Electric White Paper Library, 2011.

  • R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. D. Rose, and R. Buyya, “CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011.

  • M. Erol-Kantarci, and H. T. Mouftah, “Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues,” IEEE Communications Surveys Tutorials, vol. 17, no. 1, pp. 179-197, 2015.

  • https://www.google.com/about/datacenters/efficiency/internal/

  • Cisco, Cisco Visual Networking Index: Forecast and Methodology, 2011-2016, White Paper, May 2012. Available: http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ ns705/ns827/white paperc11-481360.pdf

  • P. Mell, and T. Grance, “The NIST definition of cloud computing,” 2011.

  • https://www.forbes.com/sites/forbestechcouncil/2017/12/15/why-energy-is-a-big-and-rapidly-growing-problem-for-data-centers/7fa591b05a30

  • Green Grid, “The Green Grid data center power efficiency metrics: PUE and DCiE,” Green Grid Report, 2007.

  • http://www.datacenterknowledge.com/archives/2011/05/10/uptime-institute-the-average-pue-is-1-8


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  • Energy Efficient Task Scheduling in Cloud Data Center

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Authors

Deepanshu Kumar
Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
Sudhanshu Kulshrestha
Assistant Professor, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract


Cloud computing is emerging as a necessary need for the IT industry in order to reduce the setup and operational cost of its infrastructure. There is a huge requirement of computing resources to satisfy customer demands. A minute delay in a service may result in a measurable amount of loss for an organization. Response time is a major metric for evaluating performance of cloud applications. Cloud data centers form backbone of cloud computing. Data centers consume enormous amount of energy. Server racks have processing units, storage and network interface. Energy is dissipated at the server racks and cooling units. Various task scheduling algorithms and virtual machine scheduling algorithms have been proposed to measure the loss in performance but the energy loss is kept at the lowest priority. The paper is focused on discussing about the two techniques that maintain a scheduled routine for tasks arriving in a data center through a simulation scenario. VM-specific scheduling of tasks is done for assignment of the tasks to single or multiple virtual machines. Comparison of the two techniques, time-shared and space-shared technique is also done to give the reader a clear view about the situation in which both techniques are used. Future work is also discussed in the same context.

Keywords


Cloud Computing, Cloudlet, Energy, Space-Shared, Time-Shared.

References