International Journal of Advanced Networking and Applications

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Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm


Affiliations
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
 

Cloud computing is now dominant in high-performance distributed computing, offering resource polling and on-demand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.

Keywords

Cloud Computing, Coronavirus Herd Immunity Optimizer Algorithm, Heterogeneous Processors, Task Scheduling.
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  • Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm

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Authors

Ahmed Y. Hamed
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
M. Kh. Elnahary
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
Hamdy H. El-Sayed
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt

Abstract


Cloud computing is now dominant in high-performance distributed computing, offering resource polling and on-demand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.

Keywords


Cloud Computing, Coronavirus Herd Immunity Optimizer Algorithm, Heterogeneous Processors, Task Scheduling.

References