Home > Research > Publications & Outputs > HUNTER:

Electronic data

  • HUNTER - sustainable cloud computing (JSS)

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Systems and Software, 184, 2022 DOI: 10.1016/j.jss.2021.111124

    Accepted author manuscript, 1.91 MB, PDF document

    Embargo ends: 22/10/22

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

HUNTER: AI based holistic resource management for sustainable cloud

Research output: Contribution to journalJournal articlepeer-review

Published
  • Shreshth Tuli
  • Sukhpal Singh Gill
  • Minxian Xu
  • Peter Garraghan
  • Rami Bahsoon
  • Scharam Dustdar
  • Rizos Sakellariou
  • Omer Rana
  • Giuliano Casale
  • Nicholas R. Jennings
Close
Article number111124
<mark>Journal publication date</mark>28/02/2022
<mark>Journal</mark>Journal of Systems and Software
Volume184
Publication StatusPublished
Early online date22/10/21
<mark>Original language</mark>English

Abstract

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Systems and Software, 184, 2022 DOI: 10.1016/j.jss.2021.111124