Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2012.36
A minimum cost maximum flow algorithm is proposed for resources(e.g. virtual machines) placement in clouds confronted to dynamic workloads and flows variations. The algorithm is compared to an exact method generalizing the classical Bin-Packing formulation using a linear integer program. A directed graph is used to model the allocation problem for cloud resources organized in a finite number of resource types; a common practice in cloud services. Providers can use the minimum cost maximum flow algorithm to opportunistically select the most appropriate physical resources toserve applications or to ensure elastic platform provisioning. The modified Bin-Packing algorithm is used to benchmark the minimum cost maximum flow solution. The latter combined with a prediction mechanism to handle dynamic variations achieves near optimal performance.
Heuristic algorithms, Resource management, Prediction algorithms, Dynamic scheduling, Virtual machining, Predictive models, Algorithm design and analysis, Minimum Cost Maximum Flow, Cloud Computing, Resource Allocation, Linear Integer Programming
Makhlouf Hadji, Djamal Zeghlache, "Minimum Cost Maximum Flow Algorithm for Dynamic Resource Allocation in Clouds", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 876-882, doi:10.1109/CLOUD.2012.36