A capacity management service for resource pools
Proceedings of the 5th international workshop on Software and performance
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Grouping genetic algorithm for solving the serverconsolidation problem with conflicts
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A trace-based service level planning framework for enterprise application clouds
Proceedings of the 7th International Conference on Network and Services Management
A goal-oriented simulation approach for obtaining good private cloud-based system architectures
Journal of Systems and Software
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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.