Renewable Energy Aware Data Centres: The Problem of Controlling the Applications Workload
Data centres are powerful facilities which aim at hosting ICT services. They have huge needs in term of power supply; furthermore the current trend is to prioritize the utilization of renewable energies over brown energies. Renewable energies tend to be very variable in time (e.g. solar energy), and thus renewable energy aware algorithms tries to schedule the applications running in the data centres accordingly. However, one of the main problems is that most of the time very little information is known about the applications running in data centres. More specifically, we need to have more information about the current and planned workload of an application, and the tolerance of that application to have its workload rescheduled. In this paper, we will first survey the problem of understanding, building information about and finally controlling the load generated by applications. Secondly we will propose hints of solutions for that problem.
KeywordsData Centre Renewable Energy Application Profile Resource Management Job Scheduling
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- 1.Dupont, C., Giuliani, G., Hermenier, F., Schulze, T., Somov, A.: An energy aware framework for virtual machine placement in cloud federated data centres. In: Proceedings of the Third International Conference of Future Energy Systems: Where Energy, Computing and Communication Meet, e-Energy (May 2012)Google Scholar
- 2.Lawler, E.: Recent results in the theory of machine scheduling. In: Mathematical Programming: The State of the Art. Springer, Berlin (1983)Google Scholar
- 3.Quan, D.-M., Basmadjian, R., De Meer, H., Lent, R., Mahmoodi, T., Sannelli, D., Mezza, F., Dupont, C.: Energy efficient resource allocation strategy for cloud data centres. In: Proceedings of the 26th International Symposium on Computer and Information Sciences, ISCIS 2011, London, UK, September 26-28, pp. 133–141. Springer (2011)Google Scholar
- 4.Bobroff, N., Kochut, A., Beaty, K.: Dynamic placement of virtual machines for managing SLA violations. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, IM 2007, pp. 119–128 (May 2007)Google Scholar
- 5.Wood, T., Shenoy, P.J., Venkataramani, A., Yousif, M.S.: Black-box and gray-box strategies for virtual machine migration. In: Proceedings of the 4th ACM/USENIX Symposium on Networked Systems Design and Implementation, Cambridge, MA, USA, p. 17. USENIX Association, Berkeley, CA (2007)Google Scholar
- 6.MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay GhemawatGoogle Scholar
- 7.Goiri, I., Le Thu, K., Nguyen, D., Guitart, J., Torres, J., Bianchini, R.: GreenHadoop: Leveraging Green Energy in Data-Processing Frameworks.Google Scholar
- 8.Goiri, et al.: GreenSlot: Scheduling Energy Consumption in Green Datacenters. In: Supercomputing (2011)Google Scholar
- 9.Epstein, J., Black, A.P., Peyton-Jones, S.: Towards Haskell in the CloudGoogle Scholar
- 10.Talaber, R., et al.: Using Virtualization to Improve Data Centre Efficiency, The Green Grid consortium (2009)Google Scholar
- 11.Claessen, K., Johansson, M., Smallbone, N., Rosen, D.: HipSpec: Automating Inductive Proofs of Program PropertiesGoogle Scholar