Frederico Alvares de Oliveira
Abstract
As a direct consequence of the increasing popularity of Cloud Computing solutions, data centers are amazingly growing and hence have to urgently face with the energy consumption issue. Available solutions rely on Cloud Computing models and virtualization techniques to scale up/down application based on their performance metrics. Although those proposals can reduce the energy footprint of applications and by transitivity of cloud infrastructures, they do not consider the internal characteristics of applications to finely define a trade-off between applications Quality of Service and energy footprint. In this paper, we propose a self-adaptation approach that considers both application internals and system to reduce the energy footprint in cloud infrastructure. Each application and the infrastructure are equipped with their own control loop, which allows them to autonomously optimize their executions. Simulations show that the approach may lead to appreciable energy savings without interfering on application provider revenues.
- D. Ardagna, B. Panicucci, M. Trubian, and L. Zhang. Energy-aware autonomic resource allocation in multi-tier virtualized environments. IEEE Transactions on Services Computing, 99(PrePrints), 2010. Google Scholar
Digital Library
- J. Arnaud and S. Bouchenak. Moka : Optimisation de services internet multi-étagés. In NOTERE, Montreal, Canada, July 2009.Google Scholar
- A. E. H. Bohra and V. Chaudhary. VMeter: Power modelling for virtualized clouds, pages 1--8. Ieee, 2010.Google Scholar
- M. Comuzzi and B. Pernici. A framework for qos-based web service contracting. ACM Trans. Web, 3(3):1--52, 2009. Google ScholarDigital Library
- F. A. de Oliveira Jr., R. Sharrock, and T. Ledoux. Synchronization of multiple autonomic control loops: Application to cloud computing. In Proceedings of the 14th International Conference on Coordination Models and Languages (Coordination) -- To appear., June 2012. Google ScholarDigital Library
- S. Frey, A. Diaconescu, and I. Demeure. Architectural integration patterns for autonomic management systems. In Proc. of the 9th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems (EASe 2012). IEEE, April 2012.Google Scholar
- G. Gauvrit, E. Daubert, and F. André. SAFDIS: A Framework to Bring Self-Adaptability to Service-Based Distributed Applications. In 36th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), pages 211--218, Lille, France, Sept. 2010. Google ScholarDigital Library
- F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller, and J. Lawall. Entropy: a consolidation manager for clusters. In VEE '09: Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, pages 41--50, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- IBM. An architectural blueprint for autonomic computing. Technical Report June, 2005.Google Scholar
- A. Kansal, J. Liu, A. Singh, R. Nathuji, and T. Abdelzaher. Semantic-less coordination of power management and application performance. SIGOPS Oper. Syst. Rev., March 2010. Google ScholarDigital Library
- A. Kansal, F. Zhao, and A. A. Bhattacharya. Virtual Machine Power Metering and Provisioning. In ACM Symposium on Cloud Computing, 2010. Google ScholarDigital Library
- J. O. Kephart, H. Chan, R. Das, D. W. Levine, G. Tesauro, F. Rawson, and C. Lefurgy. Coordinating multiple autonomic managers to achieve specified power-performance tradeoffs. In Proceedings of the Fourth International Conference on Autonomic Computing, Washington, DC, USA, 2007. IEEE Computer Society. Google ScholarDigital Library
- R. Koller, A. Verma, and A. Neogi. Wattapp: an application aware power meter for shared data centers. In Proceeding of the 7th international conference on Autonomic computing, ICAC '10, pages 31--40, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- J. G. Koomey. GROWTH IN DATA CENTER ELECTRICITY USE 2005 TO 2010. Technical report, Analytics Press, 2011.Google Scholar
- M. Làger, T. Ledoux, and T. Coupaye. Reliable dynamic reconfigurations in a reflective component model. In L. Grunske, R. Reussner, and F. Plasil, editors, Component-Based Software Engineering, volume 6092 of Lecture Notes in Computer Science, pages 74--92. Springer Berlin / Heidelberg, 2010. Google ScholarDigital Library
- R. D. Lemos and al. Software Engineering for Self-Adaptive Systems : A Second Research Roadmap (Draft Version of May 20, 2011). Technical Report October 2010, 2011.Google Scholar
- S. Mak-Karé Gueye, N. de Palma, and E. Rutten. Coordinating energy-aware administration loops using discrete control. In Proc. of the 8th International Conference on Autonomic and Autonomous Systems (ICAS 2012), March 2012.Google Scholar
- S. P. Mirashe and N. V. Kalyankar. Cloud computing. Communications of the ACM, 51(7):9, 2010.Google Scholar
- V. Petrucci, E. V. Carrera, O. Loques, J. C. B. Leite, and D. Moss. Optimized Management of Power and Performance for Virtualized Heterogeneous Server Clusters, 2011.Google Scholar
- F. Quesnel and A. Làbre. Cooperative Dynamic Scheduling of Virtual Machines in Distributed Systems. In Euro-Par 2011 Workshops, volume 7156 of Lecture Notes in Computer Science, pages 457--466, Bordeaux, France, Aug. 2011. Springer-Verlag. Google ScholarDigital Library
- F. Rossi, P. Van Beek, and T.Walsh. Handbook of Constraint Programming. Elsevier, 2006. Google ScholarDigital Library
- C. Team. choco: an open source java constraint programming library. Research report 10-02-INFO, Ecole des Mines de Nantes, 2010.Google Scholar
- H. N. Van, F. D. Tran, and J.-M. Menaud. Performance and power management for cloud infrastructures. IEEE International Conference on Cloud Computing, 2010. Google ScholarDigital Library
- A. Verma, P. De, V. Mann, T. K. Nayak, A. Purohit, G. Dasgupta, and R. Kothari. Brownmap: Enforcing power budget in shared data centers. In Middleware, pages 42--63, 2010. Google ScholarDigital Library
- P. Vromant, D. Weyns, S. Malek, and J. Andersson. On Interacting Control Loops in Self-Adaptive Systems. In Proc. of the 6th International Symposium on Software Engineeringfor Adaptive and Self-Managing Systems, pages 202--207. ACM, 2011. Google ScholarDigital Library
- X. Wang and Y. Wang. Coordinating power control and performance management for virtualized server clusters. IEEE Transactions on Parallel and Distributed Systems, 22(2):245--259, 2010. Google ScholarDigital Library
Index Terms
- Self-management of cloud applications and infrastructure for energy optimization
Recommendations
Energy aware cloud application management in private cloud data center
CSC '11: Proceedings of the 2011 International Conference on Cloud and Service ComputingCloud services decouple cloud applications from IT infrastructure in cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud data center could provide ...
Self-management of applications QoS for energy optimization in datacenters
GCM '11: Green Computing Middleware on Proceedings of the 2nd International WorkshopAs a direct consequence of the increasing popularity of Cloud Computing solutions, data centers are amazingly growing and hence have to urgently face with the energy consumption issue. Available solutions rely on Cloud Computing models and ...
Cloud Infrastructure & Applications --- CloudIA
CloudCom '09: Proceedings of the 1st International Conference on Cloud ComputingThe idea behind Cloud Computing is to deliver Infrastructure-as-a-Services and Software-as-a-Service over the Internet on an easy pay-per-use business model. To harness the potentials of Cloud Computing for e-Learning and research purposes, and to small-...
Comments