Adaptive cloud deployment using persistence strategies and application awareness
Management of large service centers and clouds requires adaptation to changing conditions and workload. Optimal deployments are possible on private clouds if there is full knowledge of the applications. Previous work, CloudOpt, combines large-scale linear optimization with a detailed non-linear performance model of every application to determine such deployments. However, when conditions change the new solution is independent of the previous deployment, and often requires excessive changes. Practical adaptive management needs a controlled compromise between the effort and cost of changing the current deployment, and the value lost by not making a change. This paper enhances CloudOpt by incorporating adjustable persistence techniques to constrain the deployment changes in each update. Three such techniques are compared, and the best ones reduce the reconfiguration effort at each adaptation step by about 90%, with about a 5%-10% penalty in running costs. Persistence techniques impose no reduction in the scalability of CloudOpt.
|Keywords||Constrained optimization, Deployment, M services computing, M.12.3 QoS management modeling, M.4.3.c solution deployment, Performance, Resource allocation|
|Journal||IEEE Transactions on Cloud Computing|
Li, J. (Jim), Woodside, C.M, Chinneck, J, & Litoiu, M. (Marin). (2015). Adaptive cloud deployment using persistence strategies and application awareness. IEEE Transactions on Cloud Computing, PP(99). doi:10.1109/TCC.2015.2409873