A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G

Lu, lYao; Liu, Lu; Panneerselvam, John; Yuan, Bo; Gu, Jiayan; Antonopoulos, Nick

Abstract

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio-economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU-SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state-of-art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real-world Cloud trace logs demonstrate that the proposed IGRU-SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.

Citation

Lu, L., Liu, L., Panneerselvam, J., Yuan, B., Gu, J., & Antonopoulos, N. (2020). A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G. IEEE Transactions on Cognitive Communications and Networking, 6(2), 486-498. https://doi.org/10.1109/tccn.2019.2954388

• Journal Article Type: Article
• Acceptance Date: Nov 9, 2019
• Online Publication Date: Nov 19, 2019
• Publication Date: 2020-06
• Deposit Date: Aug 14, 2020
• Journal: IEEE Transactions on Cognitive Communications and Networking
• Electronic ISSN: 2372-2045
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 2
• Pages: 486-498
• DOI: https://doi.org/10.1109/tccn.2019.2954388
• Public URL: http://researchrepository.napier.ac.uk/Output/2677101