Application of deterministic, stochastic, and hybrid methods for cloud provider selection

Lucas Borges De Moraes,Adriano Fiorese,Rafael Stubs Parpinelli

doi:10.1186/s13677-021-00275-1

Abstract

Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary Algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.

Highlights

Cloud Computing (CC) is a service model that allows a significant and on-demand hosting and distribution of optimized computing resources using computer networks [1] being a convenient and accessible service via Internet [2]
The Cloud Provider Selection (CPS) became a significant research challenge and several works have already been developed trying to solve it by using deterministic approaches, for example, Multi-criteria Decision Methods (MCDM) [4,5,6,7,8,9,10,11,12] Other works cope with metaheuristics to address CPS problem [13, 14]
A solution based on the Analytic Hierarchy Process (AHP) multi-criteria method is used to present feasible solutions that can be compared with the results offered by the Harris Hawks Optimizer (HHO), TLBO and Jaya

Summary

Introduction

Cloud Computing (CC) is a service model that allows a significant and on-demand hosting and distribution of optimized computing resources using computer networks [1] being a convenient and accessible service via Internet [2]. Deterministic, metaheuristic and hybrid methods, which combines deterministic ones, are well-used making them important to solve CPS problem according to state-of-the-art literature These approaches deal with CPS, the complex user requests that require more than a single provider to be satisfied, is poorly scratched. At the best of our knowledge, none work used the deterministic and de Moraes et al Journal of Cloud Computing (2022) 11:5 metaheuristics approach involving different methods in a pipeline for fulfillment of complex multi-cloud user requests. These works propose disconnected methods, particular approaches, and different problem definitions, lacking an integrative problem modelling and design. We advocate this happens because, beyond standardization absence, these works do not share a common ground such as an initial problem scenario composed of database and user requests format

Methods

Results

Conclusion