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Joint computation offloading and resource provisioning for e dge‐cloud computing environment: A machine learning‐based approach
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-09-03 , DOI: 10.1002/spe.2888
Ali Shahidinejad 1 , Mostafa Ghobaei‐Arani 1
Affiliation  

In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

中文翻译:

边缘云计算环境的联合计算卸载和资源供应:一种基于机器学习的方法

近年来,使用智能移动应用程序来促进各个领域的日常活动以提高人类生活质量的情况已广泛增加。随着智能移动应用的快速发展,边缘计算范式已经成为一种分布式计算解决方案,以支持为这些更接近移动设备的应用提供服务。由于提交给智能移动应用程序的工作负载会随着时间的推移而发生变化,因此有关卸载和边缘服务器配置以处理移动应用程序的动态工作负载的决策是资源管理范围内具有挑战性的问题之一。在这项工作中,我们利用学习自动机作为决策者将传入的动态工作负载卸载到边缘或云服务器中。此外,我们提出了一种边缘服务器配置方法,使用长短期记忆模型来估计未来的工作负载和强化学习技术,以做出适当的扩展决策。在真实和合成工作负载下获得的仿真结果表明,与其他算法相比,所提出的解决方案提高了 CPU 利用率并减少了执行时间和能耗。
更新日期:2020-09-03
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