Abstracto

An Enhanced Cost Optimization of Heterogeneous Workload Management in Cloud Computing

Sudha.C

Load Balancing is a Computer Networking method to distribute workload across multiple Computers to achieve the Optimal Resource Utilization by maximizing the Throughput, and minimizing the Response time, and also avoids overloading among the systems. The Workload comprises both transactional and long-running analytic computations. These computations may bring new performance management challenges due to the different nature of a heterogeneous set of mixed workloads. The different nature imposes the need for new scheduling mechanisms to manage co-located heterogeneous set of applications such as to run a web application and a batch job on the same physical server. The proposed system implements the Improved Cost Effective Technique(ICET) which decides, whether the resources should be leased from the public Cloud and aggregated to the private Cloud, that will provide enough processing power to execute a workflow within a given execution time. This technique is used to reduce the cost within the desired execution time. ICE Techanique works by choosing the tasks based on their dependency. Hence it does not implement any sequential order. Therefore Cloud Harmony Search Optimization (CHSO) algorithm has been proposed to include the sequential selection among the tasks. This algorithm doesn't consider the dependency of tasks. It finds the optimal solution based on the mixed workload placement matrix and generates the best new placement vector that enhances accuracy and intersection rate of harmony memory. The required simulations are done in the Cloud Simulator to investigate the performance of the algorithm