Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

Laud Charles Ochei,Julian M Bass,Andrei Petrovski

doi:10.1186/s13677-018-0124-5

Laud Charles Ochei, Julian M Bass + Show 1 more

Open Access

https://doi.org/10.1186/s13677-018-0124-5

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Abstract

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model-based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation.

Highlights

Multitenancy is an essential cloud computing property
The performance evaluation will be presented in terms of the quality of solution, robustness and the computational effort of the optimalDep algorithm when combined with any of the four different variants of metaheuristics solution:(i) HC(Random) - Hill climbing with a random solution as the initial solution; (ii) HC(Greedy) - Hill climbing with a greedy solution as the initial solution; (iii) SA(Random) - Simulated Annealing with a random solution as the initial solution; and (iv) SA(Greedy) - Simulated Annealing with a greedy solution as the initial solution
Our results show that metaheuristics which start with greedy solutions as the initial solution will require less computational effort to provide optimal solutions for deployment [72]

Summary

Introduction

Multitenancy is an essential cloud computing property. Multitenancy is a software architecture where one instance of a cloud offering is used to serve multiple tenants and/or components [1, 2]. A high degree of isolation can be achieved by deploying an application component exclusively for one tenant. We have to resolve the trade-off between a lower degree of isolation versus the possible influence that may occur between (2019) 8:1 components or a high degree of isolation versus the challenge of high resource consumption and running cost of the component. This is a decision-making problem that requires an optimal decision to be taken in the presence of a trade-off between two or more conflicting objectives [4, 5]

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