With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be$\gt$10000 times larger than the deployment cost of the 4G infrastructure. To address this fundamental limit, we propose ParallEdge, a deployment scheme that employs mobile edge servers for cost-effective service coverage. ParallEdge is designed based on the observation that the processing delay and server moving delay become comparable in many computing-intensive applications in the 5G/6G edge computing. Unlike the traditional “move-then-process” frameworks, ParallEdge follows a “move-while-processing” paradigm, which exploits the parallelism between task processing and server movement to enable resource sharing among more users. Moreover, with the joint optimization of path planning and task scheduling for multiple mobile servers, the deployment cost for service coverage can be further reduced. We analyze the approximation gap of the proposed algorithm, and conduct extensive simulation experiments based on the real-world application data, and the results show that ParallEdge can significantly reduce deployment cost and improve resource utilization compared to the state-of-the-art schemes.

Vision-based real-time object detection has become a key fundamental service for smart-city applications such as auto-drive and digital twins. Due to the limited resource available at camera devices, edge-assisted object detection has attracted increasing research attention. The existing edge-assisted schemes often assume stable or averaged wireless links during the frame offloading process. However, the assumption does not hold in real-world dynamic edge networks and will lead to significant performance degradation in terms of both detection latency and accuracy. In this paper, we propose$Kite$, a link-adaptive scheme for real-time object detection. Based on measurement studies and systematic analysis, we devise a lightweight yet representative performance indicator – “frame-anchor” distance, to incorporate the immeasurable impact of wireless dynamics into a measurable metric. Based on this performance indicator, we model the offloading process as an integer nonlinear programming problem, and propose an online link-adaptive algorithm for frame offloading decisions. We implement$Kite$in a neuro-enhanced live streaming application and conduct comparative experiments with four different datasets in WiFi/LTE based edge networks. The results show thatKitecan improve the detection accuracy by 40.53% in highly dynamic networks, compared to the state-of-the-art works.

The combination of 5G/6G and edge computing has been envisioned as a promising paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT). High deployment cost is one of the major obstacles for realizing 5G/6G edge computing. Most existing works tried to deploy the minimum number of edge servers to cover a target area by avoiding coverage overlaps. However, following this framework, the resource requirement per server will be drastically increased by the peak requirement during workload variations. Even worse, most resources will be left under-utilized for most of the time. To address this problem, we propose CoopEdge, a cost-effective server deployment scheme for cooperative multi-access edge computing. The key idea of CoopEdge is to allow deploying overlapped servers to handle variable requested workloads in a cooperative manner. In this way, the peak demands can be dispersed into multiple servers, and the resource requirement for each server can be greatly reduced. We propose a Two-step Incremental Deployment (TID) algorithm to jointly decide the server deployment and cooperation policies. For the scenarios involving multiple network operators that are unwilling to cooperate with each other, we further extend the TID algorithm to a distributed TID algorithm based on the game theory. Extensive evaluation experiments are conducted based on the measurement results of seven real-world edge applications. The results show that compared with the state-of-the-art work, CoopEdge significantly reduces the deployment cost by 38.7% and improves resource utilization by 36.2%, and the proposed distributed algorithm can achieve a comparable deployment cost with CoopEdge, especially for small-coverage servers.

Detection&tracking framework enables real-time object detection services on resource-limited mobile devices in edge networks, where mobile devices only offload few key frames to edge servers for detection and track the other frames locally. Following this framework, the offloading decisions become more important, as fewer frames can be offloaded and their results can directly affect tracking accuracy of the subsequent frames. To make wise frame offloading decisions, it is crucial to leverage the relationship between the detected frame and the tracked frame. However, the existing studies solely use the content-level information to reflect the above relationship, i.e., they tend to offload the frames with the significant pixel differences from the adjacent frames. Link impact on such relationship is completely ignored, thus leading to significant accuracy degradation. In this paper, we propose Kite, a link-adaptive and real-time object detection in dynamic edge networks, which integrates both the link-level and content-level impact into frame offloading. Kite exploits a novel performance metric, "frame-anchor" distance, to indicate the impact of dynamic wireless links. With this metric, we can incorporate both link and content information into the offloading process. The real-world experiment results show that Kite can improve the detection accuracy in dynamic edge networks.

The combination of 5G and edge computing has been envisioned as a promising paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT). High deployment cost is one of the major obstacles for realizing 5G edge computing. Most existing works tried to deploy the minimum number of edge servers to cover a target area by avoiding coverage overlaps. However, following this framework, the resource requirement per server will be drastically increased by the peak requirement during workload variations. Even worse, most resources will be left under-utilized for most of the time. To address this problem, we propose CoopEdge, a cost-effective server deployment scheme for cooperative multi-access edge computing. The key idea of CoopEdge is to allow deploying overlapped servers to handle variable requested workloads in a cooperative manner. In this way, the peak demands can be dispersed into multiple servers, and the resource requirement for each server could be greatly reduced. We further propose a two-step incremental algorithm, which jointly decides the server deployment and cooperation policies for fast convergence. We evaluate CoopEdge based on seven real-world edge applications. The results show that compared with the state-of-the-art works, CoopEdge significantly reduces the deployment cost by 38.7% and improves resource utilization by 36.2%.