An optimized capacity planning approach for virtual infrastructure exhibiting stochastic workload

Abstract

Traditionally, any capacity planning problem is modeled with deterministic workloads by considering the peak workload for resource allocation. In the context of businesses using cloud service, cloud provider could allocate resources for peak workload which could lead to under utilization of resource and charging users for unused yet provisioned resources. Hence we came up with a better capacity planning algorithm which could ensure that we plan for peak usage but do not provision for it.In our approach, we modeled the problem as a stochastic optimization problem with the objective of minimizing the number of servers considering two important constraints a) stochastic nature of workloads and b) minimizing the application SLA violations. We implemented the model using genetic algorithm and to address the stochastic nature of work loads, we reserved a free pool of resources in each server by the quantity determined by our algorithm. We evaluated the solution with real sever utilization data from a datacenter seeking consolidation. We did comparative analysis on the number of servers required suggested by our solution vs. peak work loads based solutions for various service levels. Our results illustrate that reserving certain amount of resources in servers for addressing variability of workloads gives better results in terms of lesser number of servers compared to packing resources based on peak workloads for the same service levels.