A Resource Allocation Framework with Qualitative and Quantitative SLA Classes
This paper presents a new resource allocation framework based on SLA (Service Level Agreements) classes for cloud computing environments. Our framework is proposed in the context of containers with two qualitative and two quantitative SLAs classes to meet the needs of users. The two qualitative classes represent the satisfaction time criterion, and the reputation criterion. Moreover, the two quantitative classes represent the criterion over the number of resources that must be allocated to execute a container and the redundancy (number of replicas) criterion. The novelty of our work is based on the possibility to adapt, dynamically, the scheduling and the resources allocation of containers according to the different qualitative and quantitative SLA classes and the activities peaks of the nodes in the cloud. This dynamic adaptation allows our framework a flexibility for efficient global scheduling of all submitted containers and for efficient management, on the fly, of the resources allocation. The key idea is to make the specification on resources demand less rigid and to ask the system to decide on the precise number of resources to allocate to a container. Our framework is implemented in C++ and it is evaluated using Docker containers inside the Grid’5000 testbed. Experimental results show that our framework gives expected results for our scenario and provides with good performance regarding the balance between objectives.
KeywordsScheduling and resource management Optimization Performance measurement and modelling New economic model Cloud computing Containers to support high performance computing and industrial workloads
This work is funded by the French Fonds Unique Ministériel (FUI) Wolphin Project. We thank Grid5000 team for their help to use the testbed.
- 2.Borgetto, D., Maurer, M., Costa, G.D., Pierson, J., Brandic, I.: Energy-efficient and SLA-aware management of IaaS clouds. In: International Conference on Energy-Efficient Computing and Networking, e-Energy 2012, Madrid, Spain, p. 25 (2012)Google Scholar
- 4.IBM CPLEX solver: https://www.ibm.com/products/ilog-cplex-optimization-studio
- 6.Fui-22 wolphin project: https://lipn.univ-paris13.fr/~menouer/wolphin.html
- 7.Grid5000: https://www.grid5000.fr/
- 9.Menouer, T., Cerin, C.: Scheduling and resource management allocation system combined with an economic model. In: IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA) Guangzhou, China (2017)Google Scholar
- 11.Deshmukh, S.C.: Preference ranking organization method of enrichment evaluation (promethee). Int. J. Eng. Sci. Invent. 2, 28–34 (2013)Google Scholar
- 12.Tang, C., Steinder, M., Spreitzer, M., Pacifici, G.: A scalable application placement controller for enterprise data centers. In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, pp. 331–340, May 2007Google Scholar
- 14.The apache software foundation. mesos, apache. http://mesos.apache.org/
- 15.Google cluster data traces. https://github.com/google/cluster-data/
- 16.Kubernetes scheduler. https://kubernetes.io/
- 17.Prezi real-world traces. http://prezi.com/scale/