HUNTER: AI based holistic resource management for sustainable cloud computing

Tuli, Shreshth, Gill, Sukhpal Singh, Xu, Minxian, Garraghan, Peter, Bahsoon, Rami, Dustdar, Schahram, Sakellariou, Rizos, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Buyya, Rajkumar, Casale, Giuliano and Jennings, Nicholas R. 2022. HUNTER: AI based holistic resource management for sustainable cloud computing. Journal of Systems and Software 184 , 111124. 10.1016/j.jss.2021.111124

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0164-1212
Date of Acceptance:	11 October 2021
Last Modified:	10 Nov 2022 10:04
URI:	https://orca.cardiff.ac.uk/id/eprint/145557

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