An energy and data locality aware bi-level multiobjective task scheduling model based on MapReduce for cloud computing

Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

Volumn , Issue , 2012, Pages 648-655

  Wang, Xiaoli ^a   Wang, Yuping ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

bi level multiobjective optimization; Cloud computing; Data locality; Energy efficient; MapReduce; Task scheduling

Indexed keywords

DATA LOCALITY; ENERGY EFFICIENT; GREENHOUSE GASES EMISSIONS; LOCAL SEARCH OPERATORS; MAP-REDUCE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; REDUCING ENERGY CONSUMPTION; TASK-SCHEDULING;

CLOUD COMPUTING; ENERGY EFFICIENCY; ENERGY POLICY; ENERGY UTILIZATION; GREENHOUSE GASES; MULTIOBJECTIVE OPTIMIZATION; SCHEDULING ALGORITHMS;

MULTITASKING;

EID: 84878432967     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WI-IAT.2012.90     Document Type: Conference Paper

References (21)

1. Emerson Network Power. [EB/OL] http://www.emersonnetworkpower.com/
2. Emerson Network Power. Energy Logic: Reducing Data Center Energy Consumption by Creating Savings That Cascade Across Systems. A White Paper from the Experts in Business-Critical Continuity, 2008.
3. Belady C. The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE [J]. White Paper: Metrics and Measurements, 2007.
4. ENERGY S. Report to Congress on Server and Data Center Energy Efficiency Public Law 109-431[J]. Public law, 2007,109:431.
5. Efficiency measurements. [EB/OL]. http://www.google.com/corporate/ datacenter/efficiencymeasurements.html.
6. Beloglazov A, Buyya R. Energy efficient allocation of virtual machines in cloud data centers, 2010[C]. IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.
7. Berl A, Gelenbe E, Di Girolamo M, et al. Energy-efficient cloud computing[J]. The Computer Journal, 2010,53(7):1045.
8. Buyya R, Beloglazov A, Abawajy J. Energy-Efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges[J]. Arxiv preprint arXiv:1006.0308, 2010.
9. Baliga J, Ayre R W A, Hinton K, et al. Green cloud computing: Balancing energy in processing, storage, and transport[J]. Proceedings of the IEEE, 2011,99(1):149-167.
10. Barroso L A, H Lzle U. The datacenter as a computer: An introduction to the design of warehouse-scale machines[J]. Synthesis Lectures on Computer Architecture, 2009,4(1):1-108.
11. Hamilton J. Cooperative expendable micro-slice servers (CEMS): low cost, low power servers for internet-scale services[C]. In Proceedings of CIDR 09, 2009.
12. Miller R. Google's Chiller-less Data Center [J]. Datacenter-knowledge. com, 2009.
13. Srikantaiah S, Kansal A, Zhao F. Energy aware consolidation for cloud computing, 2008[C]. USENIX Association.
14. Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters[J]. Communications of the ACM, 2008,51(1):107-113.
15. M. Erickon, A. Mayer and J. Horn, The niched Pareto genetic algorithm2 applied to the design of groundwater remediation systems, in Proc. 1st Int. Conf. Evol. Multi-Criterion Optimization, E. Zitzler, Ed., 2001, pp. 681-695.
16. K. Deb A. Pratap, S. Agrawal, and T. Meyarivan, A Fast and Elist Multiobjective Genetic Algorithm: NSGA-II, IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr. 2002.
17. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd ed. Newyork, U.S.A.: Wiley, 2001.
18. E. Zitzler, K. Deb and L. Thiele, Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput., vol. 8, pp. 173-195, Summer 2000.
19. J. Kowles and D. Corne, The Pareto archived evolution strategy: A new baseline algorithm for Pareto multiobjective optimization, 3rd ed. in Proc. Congr. Evol. Comput., 0.5em minus 0.4emWashington, D.C., Jun. 6-9, 1999, vol. 1, pp.98-105.
20. J. D. Knowles and D. W. Corne, Approximating the nondominated front using the Pareto archived evolution strategy, Evol. Comput., vol. 8, pp. 149-172, Summer 2000.
21. Q. Zhang and H. Li, MOEA/D: AMultiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Evolutionary Computation, vol. 11, no. 6, pp. 712-731, 2007. (Pubitemid 350187486)