A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud
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METHODOLOGIES AND APPLICATION
A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems Mostafa Ghobaei-Arani1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Cloud computing is one of the rapidly growing distributed computing technologies, and cloud-based applications have increased significantly in recent years. The amount of cloud resources and the number of cloud user are important metrics that affect the management of the cloud-based applications. Since the volume of traffic to cloud-based applications grows, the resource provisioning as one of challenging issues to serve time-varying and heterogeneous workloads in resource management scope to be considered. In this paper, we propose a workload clustering-based resource provisioning mechanism for executing cloud-based applications with heterogeneous workloads. Our proposed mechanism utilized biogeography-based optimization (BBO) technique with K-means clustering to classify the cloud workloads according to their quality of service (QoS) requirements. Besides, we used Bayesian learning technique to specify suitable resource provisioning actions to satisfy the QoS requirements of cloud-based applications. The simulation results obtained through simulation demonstrate that the proposed solution reduces the delay, SLA violation ratio, cost, and energy consumption compared with workload clustering-based resource provisioning mechanisms. Keywords Cloud computing Resource provisioning Bayesian learning Biogeography-based optimization
1 Introduction Cloud computing as one of the most popular computing technologies is emerged to realize the subscription-based or pay-per-use service model of computing while using Internet for accessing cloud-based applications (Buyya et al. 2009; Chandrasekaran 2014; Armbrust et al. 2010). It automatically provides a pool of IT resources such as storage, memory, network bandwidth, applications to satisfy the quality of service (QoS) requirements of cloudbased applications and the user needs. Since the cloud infrastructure is elastic, it can be utilized in cases where varying user demands and on-demand access is needed. Therefore, the cloud system management should be able to handle the cloud workload fluctuations by allocating and Communicated by V. Loia. & Mostafa Ghobaei-Arani [email protected] 1
Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
de-allocating the cloud resources in an autonomous way so that the available cloud resources adapt the cloud workloads at any moment (Barnawi et al. 2020; Ghobaei-Arani et al. 2019). Furthermore, it is required that the cloud infrastructure as the backbone of the cloud-based systems managed to serve cloud-based applications, automatically. If the infrastructure is not properly managed, then the whole cloud-based system can fail and QoS would be adversely affected. The core of cloud-based systems is re
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