Pradipta K. Banerjee

An optimized capacity planning approach for virtual infrastructure exhibiting stochastic workload

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.

Method to Fairly Distribute Power Saving Benefits in a Cloud among Various Customers

Cloud computing infrastructure offers new levels of flexibility and efficiency in a datacenter enabling more and more enterprises to adopt cloud and optimally utilize their IT infrastructure. Power management features in the platform and its exploitation through operating system policies enable significant cost savings by efficiently managing the power consumption. Most of the platform power saving features and policies are designed to exploit changes in server utilization. As the utilization decreases, more power saving features are turned-on, thereby reducing the servers power consumption. For a cloud service provider, cost savings due to power saving drives better ROI1. However for a cloud user who pays a fixed charge to the provider based on provisioned hours, there are no cost benefits due to lower power usage. In the absence of any benefit due to power savings getting passed to the end-user, there is less motivation for the cloud user to choose energy efficient policies, even for non-critical workloads. This paper describes a mechanism to pass on the benefits accrued due to power savings from cloud provider to the cloud consumer so as to encourage the consumer to choose power efficient policies and contribute to the overall reduction in power consumption and carbon footprint.