A review on various optimization techniques of resource provisioning in cloud computing

Abstract

<span lang="EN-US">Cloud computing is </span><span lang="EN-AU">the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet</span><span lang="EN-US">.It is a</span><span lang="EN-AU"> broad and deep platform that helps customers builds sophisticated, scalable applications.</span><span lang="EN-US"> To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning.</span>

An efficient resource utilization technique for scheduling scientific workload in cloud computing environment

“…However, it is observed that turning of processor might not be necessary to reduce the energy consumption, hence they proposed energyaware processor merging (EPM) mechanism to choose the particular processor to switch off for energy consumption and quick-EPM was developed to minimize the computational overheads. In [22] proposes cost and energy aware scheduling (CEAS) technique for the cloud scheduler to reduce the workflow execution cost and minimize the energy consumption which meets the deadline prerequisite. In general, CEAS comprises five algorithms, at first virtual machine (VM) selection approach is used that applies the cost utility concept to map the task to their optimal VM-types through the make span constraint.…”

Cooperative-hierarchical based edge-computing approach for resources allocation of distributed mobile and IoT applications

“…Edge computing paradigm (or similar platforms as Mobile Edge Computing (MEC), fog computing or cloudlet) was introduced, due to the fact that the mobile and IoT applications demands and resources requirements have increased, e.g. ; computing platforms [1][2][3][4][5]. Consequently, the necessity of a new platform that supports these applications demands within a close proximity of computational and storage resources, within a geographically distributed area, are really a necessity.…”