Delay Of Edge Research Articles

Joint Caching and Computing Offloading Strategies for Power Supply Chain Services Under Cloud Edge Collaborative Computing Environment

Aiming at the problem of long task calculation delay of traditional edge computing offloading strategy (COS), a joint caching and COS for power supply chain services under cloud edge collaborative computing environment is proposed by introducing the master-slave game model. First, for the network of information interaction between power grid system and electrical equipment suppliers, the corresponding overall model of cloud edge collaborative network is constructed by combining cloud computing, edge computing, supplier terminal communication and remote cloud. Then, the cloud-side collaborative network model is analyzed and solved by building the service function cache model, task unloading and data transmission model and power business data calculation model, respectively. Finally, by introducing the master-slave game model, the whole system is divided into three different interests of power service providers, communication providers and communication network terminal providers and the joint caching and COS for power supply chain services is given. Through simulation experiments, the proposed computing unloading strategy and the other three methods are compared and analyzed under the same experimental conditions. The results show that the service cache hit rate of the proposed method is the highest, reaching 96.35%. Compared with the other three comparison methods, it increased by 14.96%, 11.24% and 4.85%, respectively. In addition, its task computing delay is the least affected by agent computing power and agent computing resources, and its performance is better than the other three comparison methods.

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE–ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm’s desirable convergence property. Furthermore, the algorithm’s efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios.

Joint optimization strategy of task offloading to mobile edge computing

Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption.

Application of Cybertwin for Offloading in Mobile Multiaccess Edge Computing for 6G Networks

Multiaccess edge computing is an essential technology that academia and industry have recognized as fundamental for the future of the Internet of Things. Current research on the subject utilizes virtual machines as the intermediary between end devices and cloud servers. However, recently a new framework was proposed that utilizes Cybertwins instead of virtual machines for the same function. Such framework comes with a myriad of advantages but, most importantly, in this case, it includes a control plane capable of enabling cooperation between the Cybertwins. In this article, we present a mathematical model of the total service delay of a Cybertwin-based multiaccess edge computing system that includes user mobility, migration of virtual servers, multiple physical servers at different network tiers, fronthaul and backhaul communication, processing, and content request/caching. We also propose algorithms for guiding the operation of Cybertwins and the control plane in a multiaccess edge computing scenario. Finally, a performance analysis between Cybertwin and a virtual machine-based scheme is offered. Simulations show that Cybertwin brings significant improvement for the assumed scenario in the form of a faster overall service due to the higher cooperation. The models and simulations here were designed with the characteristics of future networks, beyond the current 5G, in mind, making them likely relevant for future networks, where multiaccess edge computing and the Internet of Things should play an even more important role.

End-to-end data paths: quickest or most reliable?

Let N=(V,E,c,l,p) be a network where V is the set of n vertices, E is the set of m edges, c(u,v)/spl ges/0 is the capacity of edge {u,v}, l(u,v)/spl ges/0 is the delay of edge {u,v}, p(u,v)/spl isin/[0,1] is the operational probability of edge {u,v}. In this letter, we present O(rm+rnlog n) time algorithms for the most reliable quickest path problem and the quickest most reliable path problem, where r is the number of different capacity values in the network.