Abstract
With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This article studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC's access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this article designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this article designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost.
Highlights
In recent years, with the explosive growth of smart devices, many emerging applications, such as AR(augmented reality, AR) [1], face recognition [2], interactive games [3], have attracted more and more attention
3) Since MEC network selection and service placement are NP-hard, this article designs an algorithm based on PSO to solve this problem
Similar to the access point selection model, this article indicates that the service placement model is as follows: xik (t) = 1, ∀t sk (t)xik (t) ≤ Ri, ∀i, t xik (t) ∈ {0, 1}, ∀i, t where xik (t) is the service placement decision variable
Summary
With the explosive growth of smart devices, many emerging applications, such as AR(augmented reality, AR) [1], face recognition [2], interactive games [3], have attracted more and more attention. To meet this challenge, a new computing paradigm, MEC (Multi-access Edge Computing, MEC) is proposed to deploy computing and storage resources from remote cloud to network edge deployment close to users [5]. A MEC system with multiple edge server services is considered to jointly optimize the access network selection and service placement of MEC. The main contributions of this article are as follows: 1) This article studies the multi-task and multi-service task offloading model with multiple MEC servers, and jointly optimizes the MEC access network selection and service placement problems. 3) Since MEC network selection and service placement are NP-hard, this article designs an algorithm based on PSO to solve this problem. 4) In the optimization process, this article designs a transition probability for selecting access points and service placement base stations.
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