An Energy and Deadline Aware Scheduling Using Invasive Weed Optimization Algorithm for Cloud Computing

Pradeep Venuthurumilli and Dr. Sridhar Mandapati

Basically, cloud computing plays significant role in present generation to produce high performance at low cost. So cloud service provider (CSP) uses heterogeneous multi user environment to obtain profit effectively. In this paper the operation of cloud system will down the cost of operation and increases the efficiency of energy. The service level agreements (SLA) give an overview about the address of cloud service provider. The service level agreements mainly consist of constraints of quality of services to achieve the deadlines of users. Here the user cannot be able to construct the service application depend on virtual machines (VM). Hence a max-min algorithm is implemented to bring down the time taken for computation. In this paper genetic algorithm (GA) with an Invasive weed optimization (IWO) algorithm is proposed. This algorithm will minimize the average make span and maximizes the utilized resources of deadline. The proposed system depends on the length of ascending order. At last a successful VM is obtained at the deadline. After this they are mapped to the suitable VM by taking minimum time for processing. From results it shows that the proposed IWO algorithm produces effective results and high performance compared to max-min scheduling or GA.

Volume 11 | 06-Special Issue

Pages: 673-682