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epcAware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing

Zakarya, Muhammad, Gillam, Lee, Ali, Hashim, Rahman, Izaz, Salah, Khaled, Khan, Rahim, Rana, Omer ORCID: and Buyya, Rajkumar 2022. epcAware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing. IEEE Transactions on Services Computing 15 (3) , pp. 1634-1648. 10.1109/TSC.2020.3005347

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The Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud for analysis, due to longer distances and delays. Fog/edge computing is a new model for analyzing and acting on time-sensitive data (real-time applications) at the network edge, adjacent to where it is produced. The model sends only selected data to the cloud for analysis and long-term storage. Furthermore, cloud services provided by large companies such as Google, can also be localized to minimize the response time and increase service agility. This could be accomplished through deploying small-scale datacenters (reffered to by name as cloudlets) where essential, closer to customers (IoT devices) and connected to a centrealised cloud through networks - which form a multi-access edge cloud (MEC). The MEC setup involves three different parties, i.e. service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management a defficult job. In the literature, various resource management techniques have been suggested in the context of what kind of services should they host and how the available resources should be allocated to customers’ applications, particularly, if mobility is involved. However, the existing literature considers the resource management problem with respect to a single party. In this paper, we assume resource management with respect to all three parties i.e. IaaS, SaaS, NaaS; and suggest a game theoritic resource management technique that minimises infrastructure energy consumption and costs while ensuring applications performance. Our empirical evaluation, using real workload traces from Google’s cluster, suggests that our approach could reduce up to 11.95% energy consumption, and approximately 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27% energy bills and NaaS can increase their costs savings up to 18.52% as compared to other methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1939-1374
Date of First Compliant Deposit: 26 June 2020
Date of Acceptance: 22 June 2020
Last Modified: 07 Nov 2022 19:13

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