Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications.

Ochei, Laud Charles; Petrovski, Andrei; Bass, Julian M.

Abstract

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.

Citation

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 the 2018 IEEE international symposium on innovations in intelligent systems and applications (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

• Conference Name: 2018 IEEE international symposium on innovations in intelligent systems and applications (INISTA 2018)
• Conference Location: Thessaloniki, Greece
• Start Date: Jul 3, 2018
• End Date: Jul 5, 2018
• Acceptance Date: Apr 15, 2018
• Online Publication Date: Jul 3, 2018
• Publication Date: Sep 30, 2018
• Deposit Date: Aug 7, 2018
• Publicly Available Date: Aug 7, 2018
• Publisher: IEEE Institute of Electrical and Electronics Engineers
• Article Number: 8466315
• DOI: https://doi.org/10.1109/INISTA.2018.8466315
• Keywords: Evolutionary computation; Cloudhosted services; Simulation based optimisation; Metaheuristics; Deployment patterns; Multitenancy isolation
• Public URL: http://hdl.handle.net/10059/3055