PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Simulation Modelling Practice and Theory

Volumn 57, Issue , 2015, Pages 142-160

  Sampaio, Altino M ^a   Barbosa, Jorge G ^b   Prodan, Radu ^c  

^a Institute Polythecnic of Porto   (Portugal)

^b UNIVERSITY OF PORTO   (Portugal)

^c UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

CPU intensive load; Energy efficiency; I O intensive load; Performance interference; QoS; SLA

Indexed keywords

ENERGY EFFICIENCY; ENERGY UTILIZATION; NATURAL RESOURCES MANAGEMENT; POWER MANAGEMENT; QUALITY OF SERVICE; RESOURCE ALLOCATION; SCHEDULING; SCHEDULING ALGORITHMS; VIRTUAL REALITY;

CPU-INTENSIVE; HETEROGENEOUS WORKLOADS; NETWORK-INTENSIVE APPLICATIONS; QUALITYOF-SERVICE REQUIREMENT (QOS); REAL WORLD ENVIRONMENTS; SLA; VIRTUAL MACHINE TECHNOLOGY; VIRTUALIZED ENVIRONMENT;

INFORMATION MANAGEMENT;

EID: 84938982201     PISSN: 1569190X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.simpat.2015.07.002     Document Type: Article

References (43)

1. T.F. Abdelzaher, K.G. Shin, and N. Bhatti Performance guarantees for web server end-systems: a control-theoretical approach IEEE Trans. Parallel Distrib. Syst. 13 1 2002 80 96
2. P. Barford, and M. Crovella Generating representative web workloads for network and server performance evaluation ACM SIGMETRICS Perform. Eval. Rev. 26 1 1998
3. A. Beloglazov, J. Abawajy, and R. Buyya Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing Future Gen. Comput. Syst. 28 5 2012 755 768
4. A. Berl, E. Gelenbe, M. Di Girolamo, G. Giuliani, H. De Meer, M.Q. Dang, and K. Pentikousis Energy-efficient cloud computing Comput. J. 53 7 2009 1045 1051
5. 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 Softw.: Pract. Exper. 41 1 2011 23 50
6. D. Carrera, M. Steinder, I. Whalley, J. Torres, Enabling resource sharing between transactional and batch workloads using dynamic application placement, in: 9th ACM/IFIP/USENIX International Conference on Middleware, December 2008, pp. 203-222.
7. R.C. Chiang, H.H. Huang, TRACON: interference-aware scheduling for data-intensive applications in virtualized environments, in: International Conference for High Performance Computing, Networking, Storage and Analysis, 2011, pp. 1-12.
8. T. Dwyer, A. Fedorova, S. Blagodurov, M. Roth, F. Gaud, J. Pei, A practical method for estimating performance degradation on multicore processors, and its application to HPC workloads, in: International Conference in High Performance Computing, Networking, Storage and Analysis, November 2012, pp. 1-11.
9. D. Ersoz, M.S. Yousif, C.R. Das, Characterizing network traffic in a cluster-based, multi-tier data center, in: 27th International Conference on Distributed Computing Systems, June 2007, pp. 59-59.
10. S.K. Garg, A.N. Toosi, S.K. Gopalaiyengar, and R. Buyya SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter J. Network Comput. Appl. 45 2014 108 120
11. S. Govindan, J. Liu, A. Sivasubramaniam, A. Kansal, Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines, in: 2nd ACM Symposium on Cloud Computing, 2011, pp. 1-14.
12. A. Greenberg, J. Hamilton, D.A. Maltz, and P. Patel The cost of a cloud: research problems in data center networks ACM SIGCOMM Comput. Commun. Rev. 39 1 2009 68 73
13. Y. Hashimoto, K. Aida, Evaluation of performance degradation in HPC applications with VM consolidation, in: 3rd International Conference on Networking and Computing (ICNC), December 2012, pp. 273-277.
14. M.R. Hines, K. Gopalan, Post-copy based live virtual machine migration using adaptive pre-paging and dynamic self-ballooning, in: ACM International Conference on Virtual Execution Environments, 2009, pp. 51-60.
15. Q. Huang, and P.P.C. Lee An experimental study of cascading performance interference in a virtualized environment ACM SIGMETRICS Perform. Eval. Rev. 40 4 2013 43 52
16. A. Iosup, S. Ostermann, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema Performance analysis of cloud computing services for many-tasks scientific computing IEEE Trans. Parallel Distrib. Syst. 22 6 2010 931 945
17. A. Jaleel, M. Mattina, B. Jacob, Last level cache (LLC) performance of data mining workloads on a CMP-a case study of parallel bioinformatics workloads, in: 12th IEEE International Symposium on High-Performance Computer Architecture, February 2006, pp. 88-98.
18. M. Keller, D. Meister, A. Brinkmann, C. Terboven, C. Bischof, eScience cloud infrastructure, in: Proceedings of the 37th EUROMICRO Conference on Software Engineering and Advanced Applications, Finland, 2011, pp. 188-195.
19. Y. Koh, R. Knauerhase, P. Brett, M. Bowman, Z. Wen, C. Pu, An analysis of performance interference effects in virtual environments, in: International Symposium on Performance Analysis of Systems and Software, April 2007, pp. 200-209.
20. G. Kousiouris, T. Cucinotta, and T. Varvarigou The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks J. Syst. Softw. 84 8 2011 1270 1291
21. L. Lei, A. Huiyao, and Z. Peng Study on last-level cache management strategy of the chip multi-processor Appl. Math. Inform. Sci. 9 2L 2015 661 670
22. S.H. Lim, J.S. Huh, Y. Kim, G.M. Shipman, and C.R. Das D-factor: a quantitative model of application slow-down in multi-resource shared systems ACM SIGMETRICS Perform. Eval. Rev. 40 1 2012 271 282
23. Z. Liu, S. Cho, Characterizing machines and workloads on a Google cluster, in: International Conference on Parallel Processing Workshops, Pittsburgh, 2012, pp. 397-403.
24. J. Mars, L. Tang, K. Skadron, and M. Lou Soffa Increasing utilization in modern warehouse-scale computers using bubble-up Micro, IEEE 32 3 2012 88 99
25. M. Maurer, I. Brandic, and R. Sakellariou Adaptive resource configuration for Cloud infrastructure management Future Gen. Comput. Syst. 29 2 2013 472 487
26. Y. Mei, L. Liu, X. Pu, S. Sivathanu, and X. Dong Performance analysis of network I/O workloads in virtualized data centers IEEE Trans. Serv. Comput. 6 1 2011 48 63
27. D. Meisner, B.T. Gold, T.F. Wenisch, PowerNap: eliminating server idle power, in: 14th International Conference on Architectural Support for Programming Languages and Operating Systems, Washington, 2009, pp. 205-216.
28. I.S. Moreno, P. Garraghan, P. Townend, and J. Xu Analysis, modeling and simulation of workload patterns in a large-scale utility cloud IEEE Trans. Cloud Comput. 2 2 2014 208 221
29. R. Nathuji, A. Kansal, A. Ghaffarkhah, Q-Clouds: managing performance interference effects for QoS-aware clouds, in: European Conference on Computer Systems, April 2010, pp. 237-250.
30. X. Pu, L. Liu, Y. Mei, S. Sivathanu, Y. Koh, C. Pu, Understanding performance interference of I/O workload in virtualized cloud environments, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), 2010, pp. 51-58.
31. J. Rao, Y. Wei, J. Gong, and C.Z. Xu QoS guarantees and service differentiation for dynamic cloud applications IEEE Trans. Network Serv. Manage. 10 1 2013 43 55
32. A.M. Sampaio, J.G. Barbosa, Estimating effective slowdown of tasks in energy-aware clouds, in: 12th IEEE Parallel and Distributed Processing with Applications (ISPA), 2014, pp. 101-108.
33. A.M. Sampaio, J.G. Barbosa, Optimizing energy-efficiency in high-available scientific cloud environments, in: IEEE International Conference on Cloud and Green Computing, Karlsruhe, 2013.
34. A.M. Sampaio, and J.G. Barbosa Towards high-available and energy-efficient virtual computing environments in the cloud Future Gen. Comput. Syst. 40 2014 30 43
35. G.A. Seber, and A.J. Lee Linear Regression Analysis vol. 936 2012 John Wiley & Sons
36. M. Silva, K.D. Ryu, D. Da Silva, VM performance isolation to support QoS in cloud, in: 26th Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), May 2012, pp. 1144-1151.
37. M. Stillwell, F. Vivien, and H. Casanova Dynamic fractional resource scheduling versus batch scheduling IEEE Trans. Parallel Distrib. Syst. 23 3 2012 521 529
38. C. Vecchiola, S. Pandey, R. Buyya, High-performance cloud computing: a view of scientific applications, in: 10th International Symposium on Pervasive Systems, Algorithms, and Networks, Taiwan, December 2009, pp. 4-16.
39. A. Verma, G. Dasgupta, T.K. Nayak, P. De, R. Kothari, Server workload analysis for power minimization using consolidation, in: 2009 Conference on USENIX Annual Technical Conference, 2009, pp. 1-14.
40. A. Verma, P. Ahuja, A. Neogi, Power-aware dynamic placement of HPC applications, in: 22nd Annual International Conference on Supercomputing, June 2008, pp. 175-184.
41. G. Welch, and G. Bishop An Introduction to the Kalman Filter 1995 University of North Carolina North Carolina
42. J. Xu, J. Fortes, A multi-objective approach to virtual machine management in datacenters, in: 8th ACM International Conference on Autonomic Computing, Karlsruhe, 2011, pp. 225-234.
43. X. Zhang, J.J. Lu, X. Qin, and X.N. Zhao A high-level energy consumption model for heterogeneous data centers Simul. Model. Pract. Theory 39 December 2013 41 55