A receding horizon approach for the runtime management of IaaS cloud systems

Proceedings - 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2014

Volumn , Issue , 2015, Pages 445-452

  Ardagna, Danilo ^a   Ciavotta, Michele ^a   Lancellotti, Riccardo ^b  

^a POLITECNICO DI MILANO   (Italy)

^b UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

Author keywords

Auto Scaling; Capacity Allocation; Optimization; QoS

Indexed keywords

OPTIMIZATION; SENSITIVITY ANALYSIS; TIME MEASUREMENT;

AUTO-SCALING; CAPACITY ALLOCATION; CLOUD-BASED APPLICATIONS; DIFFERENT TIME SCALE; QUALITYOF-SERVICE REQUIREMENT (QOS); RANDOM PERTURBATIONS; RECEDING HORIZON CONTROL; RUNTIME MANAGEMENT;

QUALITY OF SERVICE;

EID: 84924238574     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SYNASC.2014.66     Document Type: Conference Paper

References (39)

1. MODAClouds: MOdel-Driven Approach for design and execution of applications on multiple Clouds. http://www.modaclouds.eu.
2. B. Addis, D. Ardagna, B. Panicucci, M. S. Squillante, and L. Zhang. A hierarchical approach for the resource management of very large cloud platforms. IEEE Transactions on Dependable and Secure Computing 10(5):253-272, 2013.
3. J. Almeida,V. Almeida,D. Ardagna,I. Cunha,C. Francalanci,M. Trubian. Joint admission control and resource allocation in virtualized servers Journal of Parallel and Distributed Computing 70(4 344-362 2010.
4. Amazon Inc. Amazon Web Services. http://aws.amazon.com/.
5. Amazon Inc. AWS Elastic Beanstalk. http://aws.amazon.com/elasticbeanstalk/.
6. Amazon Inc. Elastic Load Balancin g. http://aws.amazon.com/elasticloadbalancing/.
7. D. Ardagna, S. Casolari, M. Colajanni, and B. Panicucci. Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems. Journal of Parallel and Distributed Computing 72(6):796-808, 2012.
8. D. Ardagna, S. Casolari, and B. Panicucci. Flexible distributed capacity allocation and load redirect algorithms for cloud systems. In IEEE Cloud 2011, 2011.
9. D. Ardagna and M. Ciavotta. Receding Horizon Auto-Scaling Algorithm for IaaS Cloud Systems. Politecnico di Milano Technical Report 2014.5. http://home.deib.polimi.it/ardagna/Cloud2014.pdf, 2014.
10. D. Ardagna, M. Ciavotta, M. Miglierina, M. Shokrolahi, G. P. Gibilisco, G. Casale, and J. F. Pérez. MODACloudML QoS abstractions and prediction models specification-initial version. Modaclouds eu project deliverable, 2013.
11. D. Ardagna, E. Di Nitto, P. Mohagheghi, S. Mosser, C. Ballagny, F. D'Andria, G. Casale, P. Matthews, C.-S. Nechifor, D. Petcu, A. Gericke, and C. Sheridan. Modaclouds: A model-driven approach for the design and execution of applications on multiple clouds. In MISE 2012 ICSE Workshop, pages 50-56, june 2012.
12. D. Ardag na, B. Panicucci, M. Trubian, and L. Zhang. Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans. Services Computing, 5(1):2-19, 2012.
13. C. Canali and R. Lancellotti. Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems. Autom. Softw. Eng., pages 319-344, 2014.
14. G. Casale, N. Mi, L. Cherkasova, and E. Smirni. Dealing with burstiness in multi-tier applications: Models and their parameterization. Software Engineering, IEEE Transactions on, 38(5):1040-1053, sept.-oct. 2012.
15. G. Casale and M. Tribastone. Fluid analysis of queueing in two-stage random environments. In QEST, pages 21-30, 2011.
16. G. Casale and M. Tribastone. Modelling exogeno us variability in cloud deployments. SIGMETRICS Perform. Eval. Rev., 40(4):73-82, Apr. 2013.
17. S. Casolari and M. Colajanni. Short-term prediction models for server management in internet-based contexts. Decis. Support Syst., 48(1):212-223, Dec. 2009.
18. W. Ellens, M. Zivkovic, J. Akkerboom, R. Litjens, and H. van den Berg. Performance of cloud computing centers with multiple pri ority classes. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 245-252, june 2012.
19. M. Ferber, T. Rauber, M. Torres, and T. Holvoet. Resource allocation for clo ud-assisted mobile applications. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 400-407, june 2012.
20. A. Gandhi, V. Gupta, M. Harchol-Balter, and M. A. Kozuch. Optimality analysis of energy-performance trade-off for server farm management. Perform. Eval., pages 1155-1171, 2010.
21. D. Gmach, J. Rolia, and L. Cherkasova. Selling t-shirts and time shares in the cloud. In Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on, pages 539-546, May 2012.
22. Google Inc. Google inc.
23. H. Goudarzi and M. Pedra m. Multi-dimensional sla-based resource allocation for multi-tier cloud computing systems. In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pages 324-331, july 2011.
24. P. Kokkinos, T. Varvarigou, A. Kretsis, P. Soumplis, and E. Varvarigos. Cost and utilization optimization of amazon ec2 instances. In Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on, pages 518-525, June 2013.
25. S. Kumar,V. Talwar,V. Kumar,P. Ranganathan,K. Schwan. Loosely coupled coordinated management in virtualized data centers Cluster Computing 14(3 259-274 2011.
26. D. Kusic, N. Kandasamy, and G. Jiang. Approximation modeling for the online performance management of distributed computing systems. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 3 8(5):1221-1233, oct. 2008.
27. D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. Power and performance management of virtualized computing environments via lookahead control. Cluster Computing, 12(1):1-15, 2009.
28. Microsoft. Microsoft corporation. http://www.microsoft.com/.
29. M. M. Nejad, L. Mashayekhy, and D. Grosu. A family of truthful greedy mechanisms for dynamic virtual machine provisioning and allocation in clouds. In Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing, CLOUD '13, pages 188-195 , Washington, DC, USA, 2013. IEEE Computer Society.
30. OMNeT++ Discrete Event Simulation System, 2014.-http://www.omnetpp.org.
31. V. Paxson and S. Floyd. Wide area traffic: The failure of poisson modeling. IEEE/ACM Trans. Netw., 3(3):226-244, June 1995.
32. K. Salah. A queueing model to achieve proper elasticity for cloud cluster jobs. In Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on, pages 755-761, June 2013.
33. B. Trushkowsky, P. Bodk, A. Fox, M. J. Franklin, M. I. Jordan, and D. A. Patterson. The scads director: Scaling a distributed storage syst em under stringent performance requirements. In Proceedings of the 9th USENIX Conference on File and Stroage Technologies, FAST'11, pages 12-12, Berkeley, CA, USA, 2011. USENIX Association.
34. E. Veloso, V. Alm eida, W. Meira, Jr., A. Bestavros, and S. Jin. A hierarchical characterization of a live streaming media workload. IEEE/ACM Trans. Netw., 14(1):133-146, Feb. 2006.
35. A. Wolke and G. Meixner. Twospot: A cloud platform for scaling out web applications dynamically. In ServiceWave, 2010.
36. Wook Hyun Kwon, Soo H. Han. Receding Horizon Predictive Control: Model Predictive Control for State Models. Springer, 2005.
37. S. Zaman and D. Grosu. An online mechanism for dynamic vm provisioning and allocation in clouds. In IEEE Cloud 2012, pages 253-260, june 2012.
38. ] L. Zhang, X. Meng, S. Meng, and J. Tan. K-scope: Online performance tracking for dynamic cloud applications. In ICAC, 2013.
39. X. Zhu, D. Young, B. Watson, Z. Wang, J. Rolia, S. Singhal, B. McKee, C. Hyser, D.Gmach, R. Gardner, T. Christian, and L. Cherkasova:. 1000 islands: An integrated approach to resource management for virtualized data centers. Journal of Cluster Computing, 12(1):45-57, 2009.