Cloud capacity planning and HSI based optimal resource provisioning

Abstract

Cloud service providers offer spot instances through highest bidding plans that are at a very economical price compared to other pricing plans, namely on-demand and reservation. The usage of spot instance enables utilization of idle resources and provide service for cost sensitive tasks. However, this approach introduces the problem of cloud capacity allocation to different pricing plans that will have impact on the task completion time. To address these issues and improve the providers revenue, in this paper a capacity planning has been carried out based on the prediction of resource requirements for each of the different resource pricing pools. The paper also presents a solution to overcome the burden faced by the service provider due to the free issue of last hour at the time of out-of-bid situation. Simulation carried out based on capacity planning along with hybrid spot instance using Amazon EC2's price show that the resource utilization is improved across the different resource pricing pools with increased number of task completion and improved provider's revenue.

A Baye's Theorem Based Node Selection for Load Balancing in Cloud Environment

“…User tasks requirements are queried along with the interaction from historical request repository to provide the request details to the capacity controller. This controller in turn plans the resource capacity among the three pricing plans that includes on-demand, reserved and spot instances resource pools [18]. The request tasks that arrive at these resource pools are deployed on the appropriate cloud node through the load balancer designed on the basis of Baye's Theorem given in Equation (1).…”

“…Simulation setup used in this paper is according to the setup used in the earlier work in [18]. Cloud resource pool of 50 heterogeneous hosts are simulated to handle 100 heterogeneous applications of different sizes and completion time that range between 1-1000 minutes are considered.…”

“…With this motivation, a Baye's theorem based node selection for load balancing (BNSLB) is proposed that identifies an optimal node for task deployment. The proposed work relies on the capacity planning presented in the earlier work in [18]. Based on the resource request made by the tasks and the available resource at the cloud node that is greater than the request made is clustered.…”