Providing a Solution for Processing Heterogeneous Tasks in Cloud Computing Using Distributed Resource Allocation

In this study, a novel approach is presented for processing heterogeneous tasks in Cloud Computing environments by leveraging optimal distributed resource allocation. The primary objective is to enhance processing efficiency and achieve effective resource utilization in conditions where tasks have diverse characteristics, varying data volumes, and different computational requirements. The proposed method models tasks and resources as a directed acyclic graph and employs a multi-objective optimization algorithm to perform resource allocation in a way that not only reduces the overall processing time but also ensures load balancing among resources. This approach, by incorporating priority queues and execution time analysis for each subtask, enables the selection of the most appropriate resource for each task. The simulation results indicate that the proposed method achieves significant improvements over conventional algorithms in reducing the overall job completion time, increasing resource utilization rates, and enhancing the quality of service in heterogeneous task processing. In this study, a novel approach is presented for processing heterogeneous tasks in Cloud Computing environments by leveraging optimal distributed resource allocation. The primary objective is to enhance processing efficiency and achieve effective resource utilization in conditions where tasks have diverse characteristics, varying data volumes, and different computational requirements. The proposed method models tasks and resources as a directed acyclic graph and employs a multi-objective optimization algorithm to perform resource allocation in a way that not only reduces the overall processing time but also ensures load balancing among resources. This approach, by incorporating priority queues and execution time analysis for each subtask, enables the selection of the most appropriate resource for each task. The simulation results indicate that the proposed method achieves significant improvements over conventional algorithms in reducing the overall job completion time, increasing resource utilization rates, and enhancing the quality of service in heterogeneous task processing.

Keywords:

Cloud Computing , heterogeneous task processing , distributed resource allocation , Multi-objective optimization , Load balancing