Abstract
By deploying edge servers on the network edge, mobile edge computing network strengthens the real-time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short-term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.
Highlights
In recent years, the popularity of mobile devices, such as smart phones or tablet computers, has a huge impact on mobile and wireless networks, which has triggered the challenges of global mobile networks
(5) The AP algorithm based on long short-term memory (LSTM) is used to train the application prediction model, and the network history information obtained in the previous step is used as the input training set to get the optimal training model
We propose the edge workload allocation scheme using the AP algorithm
Summary
The popularity of mobile devices, such as smart phones or tablet computers, has a huge impact on mobile and wireless networks, which has triggered the challenges of global mobile networks. With the trend of exponential growth of IoT devices, the transmission of terminal computing tasks and storage tasks to the cloud computing will lead to problems such as high cloud service delay, high bandwidth occupancy, and security privacy in the big data network. To solve the above problem, this paper proposes a workload allocation strategy that can be applied in real scenarios to make better use of resources in edge network and reduce the response delay of edge devices. (i) We innovatively put forward the concept of application prediction in edge computing architecture to solve edge workload allocation problem (ii) In the application prediction model, the AP algorithm based on LSTM uses historical data to predict the possible tasks of future access device.
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