Data Processing Delay Optimization in Mobile Edge Computing

Abstract

With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing.

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

“…Our team has done a lot of work on edge computing and fog computing. For example, the resource scheduling method for fog computing is studied [19], the data processing delay optimization in mobile edge computing [20], and the resource scheduling in edge computing [21], etc. In this paper, We focus on the load balancing problem of edge computing.…”

Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

“…The cloudlets represent a small scale data center which resides within the proximity to the users and minimize the transmission delay. On the other hand, authors in [15] consider data processing delay in the mobile edge computing model. Similar to the above concepts, we proposed the pre-fetcher enabled multimedia mobile cloud computing model, shown in Fig.1, where the pre-fetcher is located in close proximity to the users and contains high speed VMs and storage servers.…”

Task Scheduling for Edge Computing with Agile VNFs On‐Demand Service Model toward 5G and Beyond

“…[1, ] = 1 (8) else (9) [1, ] = [1, − 1] (10) for ← 2 to n (11) for ← 1 to (12) if (j > d i ) // case (1): the current time step j already exceeds 's deadline (13) then Virtualization technology can be applied on the CPE in many ways, such as by using the VM, container, or VM integration of container. However, a traditional VM consumes many system resources and cannot meet the requirements for light weight and service on-demand deployment.…”

“…As the network becomes increasingly flexible, software defined, and virtualized, several standards organizations are working to introduce the SDN/NFV technology into mobile networks to satisfy the low latency requirement of the 5G/IoT network for data processing and information transmission and to indirectly drive the development of the edge computing technology [7][8][9]. Edge computing is a distributed computing architecture that moves computing power for applications, data, and services from a cloud data center server to the CPE that is located close to the edge of the user network to be processed [10][11][12].…”