Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications.
Ochei, Laud Charles
Bass, Julian M.
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OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2018. Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications. In Proceedings of Institute of Electrical and Electronics Engineers (IEEE) Systems Man and Cybernetics Society (SMC) international innovations in intelligent systems and applications conference 2018 (INISTA 2018), 3-5 July 2018, Thessaloniki, Greece. New York: IEEE [online], article ID 8466315. Available from: https://doi.org/10.1109/INISTA.2018.8466315
A multitenant cloud-application that is designed to use several components needs to implement the required degree of isolation between the components when the workload changes. The highest degree of isolation results in high resource consumption and running cost per component. A low degree of isolation allows sharing of resources, but leads to degradation in performance and to increased security vulnerability. This paper presents a simulation-based approach operating on computational metaheuristics that search for optimal ways of deploying components of a cloud-hosted application to guarantee multitenancy isolation When the workload changes, an open multiclass Queuing Network model is used to determine the average number of component access requests, followed by a metaheuristic search for the optimal deployment solutions of the components in question. The simulation-based evaluation of optimization performance showed that the solutions obtained were very close to the target solution. Various recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation are also provided.