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改进了基于salp swarm算法的移动任务卸载优化

Aishwarya R¹, Mathivanan G¹

¹Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Chennai, Tamil Nadu, India.

PeerJ. Computer science

|June 26, 2025

概括

这项研究介绍了一种改进的salp swarm算法,用于移动边缘计算 (MEC),以优化任务卸载. 该ISSA-MAOA技术最大限度地减少能源消耗,内存使用和任务延迟,以提高移动应用程序的性能.

科学领域:

计算机科学计算机科学
人工智能的人工智能
移动计算移动计算

背景情况:

移动设备和物联网需要大量的计算实时视频处理和增强现实等应用程序.
边缘设备的有限能源和计算能力可能会导致高延迟和密集任务的能源消耗.
移动边缘计算 (MEC) 通过将复杂的计算卸载到边缘服务器以实现更快的处理提供了一个解决方案.

研究的目的:

解决并发任务执行的局限性,并优化MEC环境中的任务卸载.
通过找到最佳任务卸载范围来最大限度地降低移动应用程序的能源消耗和响应时间.
引入一个改进的基于salp群算法的移动应用程序卸载算法 (ISSA-MAOA),以实现高效的任务分配.

主要方法:

开发了一种改进的salp swarm算法 (ISSA),以智能地在移动设备和边缘服务器之间分配计算任务.
实施ISSA-MAOA技术,同时最大限度地减少能源消耗,内存使用和任务完成延迟.
专注于进化算法来解决任务卸载中的多目标优化问题.

主要成果:

ISSA-MAOA技术有效地优化了MEC中的任务分配,从而降低了能源消耗和延迟.
演示了改进的salp swarm算法的能力,用于增强移动云计算 (MCC) 框架.
实现了同时最大限度地减少能量,内存和缓解任务的延迟.

结论:

拟议的ISSA-MAOA为移动应用程序中卸载任务提供了更有效和更可持续的解决方案.
这项研究有助于改善资源管理和加强MEC环境中的用户交互.
这种方法可以显著提高移动云计算的整体效率.

关键词:

云计算是一种云计算.移动应用程序卸载移动应用程序卸载移动边缘计算移动边缘计算萨尔普群群算法萨尔普群群算法任务卸载任务卸载

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