Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user’s quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system’s economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP’s performance closely approximates that of the optimal scheme and surpasses existing schemes.

Mobile edge computing (MEC) is considered a promising technology to provide low-latency services by keeping computing and other resources physically close to where they are needed. The functions implemented through network function virtualization (NFV) technology in MEC are called virtual network function (VNF) instances, and the deployment and scheduling of VNF instances have always been a hot topic. The deployment refers to deploying instances on the edge servers, while scheduling refers to allocating resources to complete user requests. However, most of the existing works fail to jointly consider the deployment and scheduling of VNF instances, which cannot complete user requests reasonably and efficiently. Besides, the deployment cost can be significantly reduced by making users share the same type of instances instead of assigning one to each user. Therefore, the objective of our article is to investigate the efficient deployment and scheduling of VNF instances that are shared among different users under constraints of user delay and network resources. We first build a VNF instance deployment and scheduling model in MEC networks to study how to minimize cost and maximize network throughput under the constraints of user delay and the computing and storage resources of cloudlets. Then, taking advantage of its sharing feature, we propose a set covering-based efficient deployment and scheduling scheme called SCEDS and evaluate its performance by extensive simulations. The simulation results demonstrate the superiority of our proposed method compared to the existing ones.

As one of the significant supporting technologies for mobile virtual reality (MVR), computer vision is latency-sensitive and always requires real-time response and accurate object analysis. However, the limited computational resources of mobile devices lead to high service delay and low analysis quality, resulting in poor quality of service (QoS). By placing the edge service entities (SEs) of the video tasks on the edge server close to the mobile users, a satisfactory QoS can be obtained for MVR systems. Most of the previous works are restricted to optimizing QoS through service placement, while ignoring the key impact of the network access point and video frame resolution selections on QoS. In this paper, we propose an edge service entity placement model, which aims to jointly optimize service delay and analysis quality for MVR systems. Specifically, we design an efficient dynamic edge service entity placement scheme (EDSP-Edge) based on the block coordinate descent theory, which dynamically determines the selection strategies of network access points, service entities and video frame resolutions for users to effectively improve the QoS. We theoretically analyze the performance of EDSP-Edge and get the gap between EDSP-Edge and the optimal performance. Finally, extensive real-data driven simulations are conducted to show that the EDSP-Edge performs close to the optimal scheme and achieves at least 22% performance improvement compared with previous works.