Preview

Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS)

Advanced search

Min_c: heterogeneous concentration policy for power aware scheduling

https://doi.org/10.15514/ISPRAS-2015-27(6)-23

Abstract

In this paper, we address power aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications. Heterogeneous workloads include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the CloudSim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require.

About the Authors

F. Armenta-Cano
CICESE Research Center
Mexico


A. Tchernykh
CICESE Research Center
Mexico


J. M. Cortés-Mendoza
CICESE Research Center
Mexico


R. Yahyapour
GWDG - University of Göttingen
Germany


A. Yu. Drozdov
MIPT
Russian Federation


P. Bouvry
University of Luxembourg
Luxembourg


D. Kliazovich
University of Luxembourg
Luxembourg


A. I. Avetisyan
ISP RAS
Russian Federation


S. Nesmachnow
Universidad de la República
Uruguay


References

1. D. Kliazovich, J. E. Pecero, A. Tchernykh, P. Bouvry, S. U. Khan, A. Y. Zomaya, CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing, Journal of Grid Computing, 2015.

2. A. Beloglazov, J. Abawajy, and R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing, Future Gener. Comput. Syst., vol. 28, no. 5, pp. 755-768, May 2012.

3. J. Luo, X. Li, and M. Chen, Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers, Expert Syst. Appl., vol. 41, no. 13, pp. 5804-5816, Oct. 2014.

4. C.-H. Hsu, K. D. Slagter, S.-C. Chen, and Y.-C. Chung, Optimizing Energy Consumption with Task Consolidation in Clouds, Inf. Sci., vol. 258, pp. 452-462, Feb. 2014.

5. S. Hosseinimotlagh, F. Khunjush, and S. Hosseinimotlagh, A Cooperative Two-Tier Energy-Aware Scheduling for Real-Time Tasks in Computing Clouds, in Proceedings of the 2014 22Nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Washington, DC, USA, 2014, pp. 178-182.

6. X. Wang, X. Liu, L. Fan, and X. Jia, A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing, Math. Probl. Eng., vol. 2013, p. e878542, Aug. 2013.

7. Y. Gao, Y. Wang, S. K. Gupta, and M. Pedram, An Energy and Deadline Aware Resource Provisioning, Scheduling and Optimization Framework for Cloud Systems,” in Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, Piscataway, NJ, USA, 2013, pp. 31:1-31:10.

8. L. Luo, W. Wu, W. T. Tsai, D. Di, and F. Zhang, Simulation of power consumption of cloud data centers, Simul. Model. Pract. Theory, vol. 39, pp. 152-171, Dec. 2013.

9. Z. Liu, R. Ma, F. Zhou, Y. Yang, Z. Qi, and H. Guan, “Power-aware I/O-Intensive and CPU-Intensive applications hybrid deployment within virtualization environments,” in 2010 IEEE International Conference on Progress in Informatics and Computing (PIC), 2010, vol. 1, pp. 509-513.

10. A. Lezama, A. Tchernykh, R. Yahyapour, Performance Evaluation of Infrastructure as a Service Clouds with SLA Constraints. Computación y Sistemas 17(3): 401-411 (2013).

11. S. B. Matthias Splieth, “Analyzing the Effect of Load Distribution Algorithms on Energy Consumption of Servers in Cloud Data Centers,” 2015.

12. A. Tchernykh, L. Lozano, U. Schwiegelshohn, P. Bouvry, J. Pecero, S. Nesmachnow: Energy-Aware Online Scheduling: Ensuring Quality of Service for IaaS Clouds. International Conference on High Performance Computing & Simulation (HPCS 2014), pp 911-918, Bologna, Italy (2014).

13. A. Tchernykh, U. Schwiegelsohn, R. Yahyapour, N. Kuzjurin: Online Hierarchical Job Scheduling on Grids with Admissible Allocation, Journal of Scheduling 13(5):545-552 (2010)

14. A. Tchernykh, J. Ramírez, A. Avetisyan, N. Kuzjurin, D. Grushin, S. Zhuk,: Two Level Job-Scheduling Strategies for a Computational Grid. In R. Wyrzykowski et al. (eds.) Parallel Processing and Applied Mathematics, 6th International Conference on Parallel Processing and Applied Mathematics. Poznan, Poland, 2005, LNCS 3911, pp. 774-781, Springer-Verlag (2006).

15. B. Dorronsoro, S. Nesmachnow, J. Taheri, A. Zomaya, E-G. Talbi, P. Bouvry: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustainable Computing: Informatics and Systems 4:252-261 (2014).

16. A. Tchernykh, J. Pecero, A. Barrondo, E. Schaeffer: Adaptive Energy Efficient Scheduling in Peer-to-Peer Desktop Grids, Future Generation Computer Systems, 36:209-220 (2014).

17. J.M. Ramírez, A. Tchernykh, R. Yahyapour, U. Schwiegelshohn, A. Quezada, J. González, A. Hirales: Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids. Journal of Grid Computing 9:95-116 (2011).

18. S. Iturriaga, S. Nesmachnow, B. Dorronsoro, P. Bouvry: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Computing and Informatics 32(2):273-294 (2013)

19. U. Schwiegelshohn, A. Tchernykh: Online Scheduling for Cloud Computing and Different Service Levels, 26th Int. Parallel and Distributed Processing Symposium Los Alamitos, CA, pp. 1067-1074 (2012).

20. A. Tchernykh, L. Lozano, U. Schwiegelshohn, P. Bouvry, J. Pecero, S. Nesmachnow, A. Drozdov: Online Bi-Objective Scheduling for IaaS Clouds with Ensuring Quality of Service. Journal of Grid Computing, Springer-Verlag, DOI 10.1007/s10723-015-9340-0 (2015).

21. Parallel Workload Archive [Online, November 2014]. Available at http://www.cs.huji.ac.il/labs/parallel/ workload

22. Grid Workloads Archive [Online, November 2014]. Available at http://gwa.ewi.tudelft.nl

23. E. Zitzler: Evolutionary algorithms for multiobjective optimization: Methods and applications, PhD thesis, Swiss Federal Institute of Technology. Zurich (1999)

24. D. Tsafrir, Y. Etsion, D. Feitelson: Backfilling Using System-Generated Predictions Rather than User Runtime Estimates. IEEE Transactions on Parallel and Distributed Systems 18 (6), pp.789-803 (2007)

25. F. Armenta-Cano, A. Tchernykh, J. M. Cortés-Mendoza, R. Yahyapour, A. Drozdov, P. Bouvry, D. Kliazovich, A. Avetisyan: Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service. Proceedings of the 1st Russian Conference on Supercomputing - Supercomputing Days 2015, Moscow, Russia, September 28-29, 2015. Editors V. Voevodin, S. Sobolev. Published on CEUR-WS: 22-Oct-2015, Vol-1482, p. 687-697. ONLINE: http://ceur-ws.org/Vol-1482/, URN: urn:nbn:de:0074-1482-7


Review

For citations:


Armenta-Cano F., Tchernykh A., Cortés-Mendoza J.M., Yahyapour R., Drozdov A.Yu., Bouvry P., Kliazovich D., Avetisyan A.I., Nesmachnow S. Min_c: heterogeneous concentration policy for power aware scheduling. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2015;27(6):355-380. (In Russ.) https://doi.org/10.15514/ISPRAS-2015-27(6)-23



Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2079-8156 (Print)
ISSN 2220-6426 (Online)