Efficient Directed Acyclic Graph Scheduling In Order To Balance Load At Cloud

Abstract Cloud computing has offered services related to utility aligned IT services. Reducing the schedule length is considered as one of the significant QoS need of the cloud provider for the satisfaction of budget constraints of an application. Scheduling computing tasks in the processor is the key issue of Advanced Computing. Scheduling list become a topic of conversation for the developers because it helps to solve high complexity with minimum complexity and thus estimates additional scheduling of application matrix problem. The main aim is to decrease the overall time to complete a task. For reducing the computation cost and earlier finish time of the system prioritization of the jobs will be done. Task scheduling in a parallel environment is one of the NP (Non deterministic polynomial) problems, which deals with the optimal assignment of a task. To deal with the favorable assignment of some task, task scheduling is considered as one of the NP problem. In this research, for balancing the load algorithm DAG and HEFT will be used. To reduce the load and total execution time jobs will be executed in an order. For the optimization of the traditional scheduling and balancing algorithm, an algorithm has been designed for reducing the delay. DVFS is used to perform the tasks in less time. The job placement also has a great impact on the cost computation. Here, the placement is done by using Optimization algorithm that is Bee colony optimization (BCO) algorithm for generating high-quality solutions for optimizing and searching the problems by depending on bio-inspired operator, namely mutation, crossover and selection. Metrics namely, make span, CCR (Computation Cost Ratio) and Energy consumption are used for the evaluation of the proposed work. All the simulations will be carried out in CLOUDSIM environment. Keywords: CLOUDSIM, Computation cost ratio (CCR), Bee colony optimization (BCO),Dynamic voltage and frequency scaling (DVFS),Make span, energy consumption, DAG, HEFT