Energy Saving Strategy for Task Migration Based on Genetic Algorithm

Abstract

With the rapid development and popularization of mobile Internet and Internet of Things, People have entered the era of Internet of Everything. Mobile edge computing is the key technology to improving the user experience of 5G network in the future, which is close to the data source, so that it can effectively reduce the network transmission delay. Edge computing will sink the business to the edge of the network, provide computing services and storage services, and provide task migration platform for users. At present, the current battery development speed of mobile devices is far behind the development speed of its processor and memory. Therefore, to solve the problem of “how to realize the low energy migration of complex dependency application”, a fine-grained directed acyclic graph task partition model is established based on the characteristics of mobile edge computing, and the relationships among the divided subtasks are analyzed to construct the minimization energy consumption problem under the execution time limit, then uses the genetic algorithm to find the optimal solution, and obtains the result of the migration decision for each sub task, that is, the optimal energy-saving migration plan for the entire mobile terminal application. The experimental results show that the fine-grained task migration strategy proposed in this paper makes full use of the advantages of mobile edge computing, and can effectively reduce the energy consumption of mobile terminal devices on the premise of meeting the task execution delay.