Abstract
Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment.
Highlights
With the rapid development of the mobile Internet and the Internet of Things (IoT), as well as the emergence of various new types of services, users are increasingly demanding better quality network services
In order to effectively solve the challenges of high load and low latency caused by the development of the Internet, the concept of multi-access edge computing (MEC) has been proposed and it has attracted the attention of academics and industry
In this paper, due to the low latency and relatively limited capability of the edge cloud, we study the problem of task caching for MEC, and propose a joint optimization of task caching and task offloading with complex dependencies using the fine-grained partitioning model to solve the problem of energy consumption optimization using the genetic algorithm, while satisfying user requirements
Summary
With the rapid development of the mobile Internet and the Internet of Things (IoT), as well as the emergence of various new types of services, users are increasingly demanding better quality network services. Defined as mobile edge computing, MEC offers application developers and content providers cloud-computing capabilities and an IT service environment at the edge of the network. This environment is characterized by ultra-low latency and high bandwidth as well as real-time access to radio network information that can be leveraged by applications. In this paper, due to the low latency and relatively limited capability of the edge cloud, we study the problem of task caching for MEC, and propose a joint optimization of task caching and task offloading with complex dependencies using the fine-grained partitioning model to solve the problem of energy consumption optimization using the genetic algorithm, while satisfying user requirements.
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