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Hamed, Ahmed Y.
- Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm
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Authors
Affiliations
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, EG
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, EG
Source
International Journal of Advanced Networking and Applications, Vol 14, No 6 (2023), Pagination: 5686-5695Abstract
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.Full Text
References
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- Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm
Abstract Views :45 |
PDF Views:1
Authors
Affiliations
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, EG
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, EG
Source
International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5853-5860Abstract
In cloud computing systems, task scheduling is crucial. Task scheduling cannot be done based on a single criterion but rather on rules and regulations that may be referred to as an agreement between cloud customers and providers. This agreement is nothing more than the user's desire for the providers to offer the kind of service that they expect. Providing high-quality services to consumers under the deal is a critical duty for providers, who must also manage many responsibilities. The task scheduling problem may be considered the search for an ideal assignment or mapping of a collection of subtasks of distinct tasks across the available set of resources to meet the intended goals for tasks. This paper proposes an efficient scheduling task algorithm based on the social group optimization of cloud computing systems. By applying it to three cases, we evaluate the performance of our algorithm. The findings suggest that the proposed strategy successfully achieved the best solution in Makespan, Speedup, Efficiency, and Throughput.Keywords
Heterogeneous resources, Social Group Optimization Algorithm, Task scheduling, Cloud ComputingFull Text
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