Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan, Ayoub; Itradat, Awni; Al-Dubai, Ahmed Y.; Zomaya, Albert Y.; Min, Geyong

Abstract

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions.

Citation

Alsarhan, A., Itradat, A., Al-Dubai, A. Y., Zomaya, A. Y., & Min, G. (2018). Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments. IEEE Transactions on Parallel and Distributed Systems, 29(1), 31-42. https://doi.org/10.1109/tpds.2017.2748578

• Journal Article Type: Article
• Acceptance Date: Aug 22, 2017
• Online Publication Date: Sep 7, 2017
• Publication Date: Jan 1, 2018
• Deposit Date: Aug 22, 2017
• Publicly Available Date: Aug 23, 2017
• Journal: IEEE Transaction on Parallel and Distributed Systems
• Print ISSN: 1045-9219
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 1
• Pages: 31-42
• DOI: https://doi.org/10.1109/tpds.2017.2748578
• Keywords: Multi-service, cloud environments, resources,
• Public URL: http://researchrepository.napier.ac.uk/Output/978545
• Contract Date: Aug 22, 2017