Evaluating auto-scaling strategies for cloud computing environments

Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS

Volumn 2015-February, Issue February, 2015, Pages 187-196

  Netto, Marco A S ^a   Cardonha, Carlos ^a   Cunha, Renato L F ^a   Assuncao, Marcos D ^b  

^a IBM RESEARCH   (United States)

^b UNIV LYON   (France)

Author keywords

Auto scaling; Auto scaling Demand Index; Cloud Computing; Elasticity

Indexed keywords

BENCHMARKING; ELASTICITY;

AUTO-SCALING; AUTO-SCALING DEMAND INDEX; CLOUD COMPUTING ENVIRONMENTS; LOWER AND UPPER BOUNDS; PERFORMANCE INDICATORS; RESOURCE WASTES; SCALING PARAMETER; WORKLOAD CHARACTERISTICS;

CLOUD COMPUTING;

EID: 84937827940     PISSN: 15267539     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MASCOTS.2014.32     Document Type: Conference Paper

References (22)

1. "Google cluster-usage traces: Format + schema, " 2013. [Online]. Available: Https://code.google.com/p/googleclusterdata/
2. J. Blazewicz K. Ecke G. Schmidt J. Weglarz, Scheduling in Computer and Manufacturing Systems, 2nd ed. 1994.
3. M. L. Pinedo, Planning and Scheduling in Manufacturing and Services, 2nd ed., 2007.
4. D. Tsafrir, Y. Etsion, and D. G. Feitelson, "Backfilling using system-generated predictions rather than user runtime estimates, " IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 6, pp. 789-803, 2007.
5. T. D. Braun et al., " A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems," Journal of Parallel and Distributed computing, 2001.
6. D. G. Feitelson, L. Rudo lph, U. Schwiegelshohn, K. C. Sevcik, and P. Wong, " Theory and practice in parallel job scheduling," in Proc. of the Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP'97), 1997.
7. N. D. Doulamis, A. D. Doulamis, E. A. Varvarigos, and T. A. Varvarigou, " Fair scheduling algorithms in grids," IEEE Transactions on Parallel and Distributed Systems, 2007.
8. J.-F. Pineau, Y. Robert, and F. Vivien, "Energy-aware scheduling of bag-of-tasks applications on master-worker platforms, " Concurrency and Computation: Practice and Experience, vol. 23, no. 2, pp. 145-157, 2011.
9. M. D. Assunçao et al., " Context-aware job scheduling for cloud computing environments," in Proc. of the IEEE Int. Conf. on Utility and Cloud Computing (UCC), 2012.
10. C. B. Lee and A. Snavely, "Precise and realistic u tility functions for user-centric performance analysis of schedulers," in Proc. of the Int. Symp. on High-Performance Distributed Computing (HPDC'07), 2007.
11. A. AuYoung et al., "Service contracts and aggregate u tility functions," in Proc. of the 15th IEEE Int. Symp. on High Performance Distributed Computing (HPDC'06), 2006.
12. "Amazon CloudWatch, " 2014. [Online]. Available: Http://docs.aws.amazon.com/AWSEC2/latest /User-Guide/using-cloudwatch.html
13. "Microsoft Azure, " 2014. [Online]. Available: Http://www.windowsazure.com/enus/documentation/articles/cloud-services-how-to-scale/
14. "Scryer: Netfix's Predictive Auto Scaling Engine, " 2014. [Online]. Available: Http://techblog.netflix.com/2013/12/scryer-netflixspredictive-auto-scaling.html
15. M. Mao, J. Li, and M. Humphrey, "Cloud auto-scaling with deadline and budget constraints, " in Proc. of the 11th IEEE/ACM International Conference on Grid Computing (GRID). IEEE/ACM, 2010, pp. 41-48.
16. Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, " Cloudscale: Elastic resource scaling for multi-tenant cloud systems," in Proc. of the 2nd ACM Symposium on Cloud Computing. ACM, 2011, p. 5.
17. Y. Chen, S. Alspaugh, and R. Katz, " Interactive analytical processing in big data systems: A cross-industry study of mapreduce workloads," Proc. VLDB Endow., 2012.
18. Z. Fadika and M. Govindaraju, "DELMA: Dynamically ELastic MapReduce Framework for CPU-Intensive Applications," in Proc. of the 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid' 11), 2011, pp. 454-463.
19. M. Sedaghat, F. Hernandez-Rodriguez, and E. Elmroth, " A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling," in Proc. of the ACM Cloud and Autonomic Computing Conference (CAC'13). ACM, 2013.
20. M. Mao and M. Humphrey, "Auto-scaling to minimize cost and meet application deadlines in cloud workflows, " in Proc. of the Int. Conf. for High Performance Computing, Networking, Storage and Analysis (SC), 2011.
21. A. Ali-Eldin, J. Tordsson, E. Elmroth, and M. Kihl, " Workload classification for efficient auto-scaling of cloud resources," Technical Report, vol. 2013, 2005. [Online]. Available: Http://www8.cs.umu.se/research/uminf/reports/-2013/013/part1.pdf
22. R. L. F. Cunha, M. D. Assunçao, C. Cardonha, and M. A. S. Netto, " Exploiting user patience for scaling resource capacity in cloud services," in Proc. of the 7th IEEE International Conference on Cloud Computing (CLOUD'14), 2014.