survey

Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing: A Survey

Authors:

Rafael García Leiva,

Per-Olov ÖstbergAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 5

Article No.: 94, Pages 1 - 39

https://doi.org/10.1145/3341145

Published: 13 September 2019 Publication History

Abstract

Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks—a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use.

This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 52, Issue 5

September 2020

791 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3362097

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

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Publication History

Published: 13 September 2019

Accepted: 01 June 2019

Revised: 01 March 2019

Received: 01 October 2018

Published in CSUR Volume 52, Issue 5

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