An Energy Aware Cost Effective Scheduling Framework for Heterogeneous Cluster System

Future Generation Computer Systems

Volumn 71, Issue , 2017, Pages 73-88

  Kumar, Neetesh ^a   Vidyarthi, Deo Prakash ^b  

^a ABV INDIAN INSTITUTE OF INFORMATION TECHNOLOGY AND MANAGEMENT   (India)

^b JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Dynamic Voltage and Frequency Scaling (DVFS); Green SLA; Heterogeneous Cluster System (HCS); Job scheduling; Makespan

Indexed keywords

CLUSTER ANALYSIS; CLUSTER COMPUTING; COST EFFECTIVENESS; COSTS; DISTRIBUTED DATABASE SYSTEMS; DYNAMIC FREQUENCY SCALING; ENERGY UTILIZATION; ENVIRONMENTAL PROTECTION; POWER MANAGEMENT; SCHEDULING; VOLTAGE SCALING;

DYNAMIC VOLTAGE AND FREQUENCY SCALING; GREEN SLA; HETEROGENEOUS CLUSTERS; JOB SCHEDULING; MAKESPAN;

ENERGY EFFICIENCY;

EID: 85012266395     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.01.015     Document Type: Article

References (36)

1. [1] Giacobbe, Maurizio, Celesti, Antonio, Fazio, Maria, Villari, Massimo, Puliafito, Antonio, Towards energy management in cloud federation: A survey in the perspective of future sustainable and cost-saving strategies. Comput. Netw. 91 (2015), 438–452.
2. [2] Kaplan, J.M., Forest, W., Kindler, N., Revolutionizing data center energy efficiency, technical report., 2008, Mckinsey and Company.
3. [3] Gmach, D., Chen, Y., Shah, A., Rolia, J., Bash, C., Christian, T., Sharma, R., Profiling sustainability of data centers. 2010 IEEE International Symposium on Sustainable Systems and Technology, (ISSST), 2010, IEEE, 1–6.
4. [4] Lee, Y.C., Zomaya, A.Y., Energy conscious model for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22:8 (2011), 1374–1381.
5. [5] Zhu, X., He, C., Li, K., Qin, X., Adaptive energy-efficient model for real-time jobs on dvs-enabled heterogeneous clusters. J. Parallel Distrib. Comput. 72:6 (2012), 751–763.
6. [6] Wang, L., Khan, S.U., Chen, D., Kołodziej, J., Ranjan, R., Xu, C.-z., Zomaya, A., Energy-aware parallel job model in a cluster. Future Gener. Comput. Syst. 29:7 (2013), 1661–1670.
7. [7] Baskiyar, S., Abdel-Kader, R., Energy-aware dag model on heterogeneous systems. Cluster Comput. 13:4 (2010), 373–383.
8. [8] Q. Huang, S. Su, J. Li, P. Xu, K. Shuang, X. Huang, Enhanced energy-efficient model for parallel applications in cloud, in: 12th IEEE/ACM International Symposium Cluster, Cloud and Grid Computing, CCGrid, 2012, 2012, pp. 781–786.
9. [9] Wu, C.-M., Chang, R.-S., Chan, H.-Y., A green energy-efficient model using the dvfs technique for cloud datacenters. Future Gener. Comput. Syst. 37 (2014), 141–147.
10. [10] K.H. Kim, R. Buyya, J. Kim, Energy aware model of bag-of-jobs applications with deadline constraints on dvs-enabled clusters, in: CCGRID, Vol. 7, 2007, pp. 541–548.
11. [11] Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F., Liu, L., Service level agreement based energy-efficient resource management in cloud data centers. Comput. Electr. Eng. 40:5 (2014), 1621–1633.
12. [12] Marzolla, M., Mirandola, R., Dynamic energy management for qos-aware applications. Sustain. Comput.: Inform. Syst. 3:4 (2013), 231–248.
13. [13] Haque, M.E., Le, K., Goiri, Íñigo, Bianchini, R., Nguyen, T.D., Providing green slas in high-performance computing clouds. 2013 International Green Computing Conference, (IGCC), 2013, IEEE, 1–11.
14. [14] Kumar, Neetesh, Vidyarthi, D.P., Improved scheduler for multi-core many–core systems. J. Comput. 96:11 (2014), 1087–1110 Springer.
15. [15] https://drive.google.com/open?id=0B7P0JfSxxK-wN1ozUWtFelVaeGs.
16. [16] Topcuouglu, H., Hariri, S., Wu, M.Y., Performance effective and low-complexity job model for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13:3 (2002), 60–274 2.
17. [17] Nie, P., Duan, Z., Efficient and scalable scheduling for performance heterogeneous multicore systems. J. Parallel Distrib. Comput. 72 (2012), 353–361.
18. [18] Beloglazov, A., Abawajy, J., Buyya, R., Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28:5 (2012), 755–768.
19. [19] Da Costa, Georges, de Assunção, Marcos Dias, Gelas, Jean-Patrick, Georgiou, Yiannis, Lefèvre, Laurent, Orgerie, Anne-Cécile, Pierson, Jean-Marc, Richard, Olivier, Sayah, Amal, Multi-facet approach to reduce energy consumption in clouds and grids: the green-net framework. Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, e-Energy’10, 2010, ACM, New York, NY, USA, 95–104.
20. [20] Borgetto, Damien, Maurer, Michael, Da-Costa, Georges, Pierson, Jean-Marc, Brandic, Ivona, Energy-efficient and SLA-aware management of IaaS clouds. Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet, e-Energy’12, 2012, ACM, New York, NY, USA, 25:1–25:10.
21. [21] Koch, Fernando, Assunção, Marcos D., Cardonha, Carlos, Netto, Marco A.S., Optimising resource costs of cloud computing for education. Future Gener. Comput. Syst. 55 (2016), 473–479.
22. [22] Paul, Debdeep, Zhong, Wen-De, Bose, Sanjay K., Energy efficiency aware load distribution and electricity cost volatility control for cloud SPs. J. Netw. Comput. Appl. 59 (2016), 185–197.
23. [23] Paul, Debdeep, Zhong, Wen-De, Bose, Sanjay K., Energy efficient cloud service pricing: A two-timescale optimization approach. J. Netw. Comput. Appl. 64 (2016), 98–112.
24. [24] K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, T.D. Nguyen, Reducing electricity cost through virtual machine placement in high performance computing clouds, in: SC, 2011, p. 22.
25. [25] Bila, N., de Lara, E., Hiltunen, M., Joshi, K., Lagar Cavilla, H.A., Satyanarayanan, M., The case for energy oriented partial desktop migration. Proceedings of the 2Nd USENIX Conference on Hot Topicsin Cloud Computing, in: Hot Cloud’10, 2010, USENIX Association, Berkeley, CA, USA, 3.
26. [26] Goiri, I.n., Katsak, W., Le, K., Nguyen, T.D., Bianchini, R., Parasol and greens witch: Managing data centers energyed by renewable energy. SIGPLAN Not. 48:4 (2013), 51–64.
27. [27] H. Li, C. Wu, Z. Li, F.C.M. Lau, Profit maximizing virtual machine trading in a federation of selfish clouds, in: INFOCOM, 2013, pp. 25–29.
28. [28] K. Le, R. Bianchini, J. Zhang, Y. Jaluria, J. Meng, T.D. Nguyen, Reducing electricity cost through virtual machine placement in high performance computing clouds, in: SC Conf., Vol. SC 11, November 12–18, 2011, pp. 1–12.
29. [29] R. Buyya, R. Ranjan, R.N. Calheiros, Inter cloud: utility oriented federation of cloud computing environments for scaling of application services, in: ICA3, Vol. PP, No. 1, 2010, pp. 13–31.
30. [30] Kyong Hoon Kim, Rajkumar Buyya, Jong Kim, Energy aware Algorithm of bag of- tasks applications with deadline constraints on DVS-enabled clusters, in: CCGRID, 2007, pp. 541–548.
31. [31] Mei, Jing, Li, Kenli, Hu, Jingtong, Yin, Shu, Shab, Edwin H.-M., Energy-aware preemptive scheduling model for sporadic tasks on DVS platform. Microprocess. Microsyst. 37 (2013), 99–112.
32. [32] http://blog.cloudharmony.com/2010/05/what-is-ecu-cpu-benchmarking-in-cloud.html.
33. [33] Baranwal, Gaurav, Vidyarthi, Deo Prakash, A cloud service selection model using improved ranked voting method. Concurr. Comput.: Pract. Exper., 2016, 10.1002/cpe.3740 Wiley Online Library ( wileyonlinelibrary.com).
34. [34] Lee, Y.C., Zomaya, A.Y., Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22 (2011), 1374–1381.
35. [35] Q. Huang, S. Su, J. Li, P. Xu, K. Shuang, X. Huang, Enhanced energy-efficient scheduling for parallel applications in cloud, in: 12th IEEE/ACM International Symposium Cluster, Cloud and Grid Computing, CCGrid, 2012, 2012, pp. 781–786.
36. [36] Tang, Zhuo, Qi, Ling, Cheng, Zhenzhen, Li, Kenli, Khan, Samee U., Li, Keqin, An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput., 2015, 10.1007/s10723-015-9334-y.