一种基于遗传算法的多站点协同计算卸载算法

A genetic-based multi-site collaborative computation offloading algorithm

边缘计算为资源受限的物联网IoT设备扩展计算资源、增强存储容量,可以改善IoT应用程序的执行性能。在IoT环境中,大多数应用都将以分布式架构的形式部署在各站点中,站点之间需要协作完成任务。为了解决物联网环境中多站点协同计算的代价优化问题,提出了一种基于遗传算法的多站点协同计算卸载算法GAMCCO。该算法将应用程序抽象为任务依赖关系图模型,分析各任务之间的依赖关系,将多站点协同计算卸载的问题建模为代价模型,并利用遗传算法寻找最小代价的卸载方案。实验与评估结果表明,所提出的GAMCCO算法可以有效减少IoT应用的时延,同时降低终端设备的能耗。

Edge computing,extending computing resources and enhancing storage capacity for resource-constrained Internet of Things(IoT)devices,can improve the performance of IoT applications.In an IoT environment,most applications will be deployed at multi-sites in a distributed architecture,and these sites will need to collaborate to finish a service.In order to solve the cost optimization problem of multi-site collaborative computation in the IoT environment,a genetic-based multi-site collaborative computation offloading algorithm(GAMCCO)is proposed.The algorithm models the application into a task relation graph and analyzes the dependencies among tasks.Afterwards,the multi-site collaborative offloading problem is formulated in terms of execution cost,and genetic algorithm is used to find the best offloading scheme.Experimental and evaluation results show that the proposed GAMCCO algorithm can effectively reduce the delay of IoT applications and the energy consumption of terminal devices.