Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
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TL;DR: This research proposes a method to conduct calculations in a collaborative way to alleviate the huge computing pressure caused by the single mobile edge server computing mode as the amount of data increases.
96 citations
TL;DR: The UAV is utilized as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and a proposed approximation algorithm is proposed that is superior to three baseline algorithms to minimize the average latency of all UEs.
Abstract: Advances in wireless communications are empowering the emerging Internet-of-Things (IoT) applications and services with billions of connected devices Mobile-edge computing (MEC) has been proposed to reduce the round-trip delay of these applications as IoT devices may have limited computing resources and the resource-rich mobile cloud may be far away On the other aspect, unmanned aerial vehicles (UAVs) may potentially be employed to improve the quality of service and the channel conditions of users We thus propose to utilize the UAV as a computing node as well as a relay node to improve the average user latency in the UAV-aided MEC (UAV-MEC) network and formulate the UAV-MEC problem with the objective to minimize the average latency of all UEs As the UAV-MEC problem is NP-hard, we decompose it into three subproblems We propose an approximation algorithm with low complexity to solve the first subproblem and then we obtain the optimal solutions of the remaining two subproblems, upon which another proposed approximation algorithm employs these solutions to finally solve the UAV-MEC problem The evaluation results demonstrate that the proposed algorithm is superior to three baseline algorithms
96 citations
TL;DR: In this article, the authors present a survey of the literature that has applied theoretical game theory to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC), and highlight future trends and research directions for applying theoretical model games in upcoming MEC services.
Abstract: Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game’s usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game’s model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.
96 citations
TL;DR: This paper develops, implements, and evaluates Chain-based Low latency VNF ImplemeNtation (CALVIN), a low-latency management framework for distributed Service Function Chains (SFCs), and investigates the practical feasibility of NFV with respect to the tactile Internet latency requirements.
Abstract: Software-defined networking (SDN) and network function virtualization (NFV) processed in multi-access edge computing (MEC) cloud systems have been proposed as critical paradigms for achieving the low latency requirements of the tactile Internet. While virtual network functions (VNFs) allow greater flexibility compared to hardware-based solutions, the VNF abstraction also introduces additional packet processing delays. In this paper, we investigate the practical feasibility of NFV with respect to the tactile Internet latency requirements. We develop, implement, and evaluate Chain-based Low latency VNF ImplemeNtation (CALVIN), a low-latency management framework for distributed Service Function Chains (SFCs). CALVIN classifies VNFs into elementary, basic, and advanced VNFs; moreover, CALVIN implements elementary and basic VNFs in the kernel space, while the advanced VNFs are implemented in the user space. Throughout, CALVIN employs a distributed mapping with one VNF per Virtual Machine (VM) in a MEC system. Furthermore, CALVIN avoids the metadata structure processing and batch processing of packets in the conventional Linux networking stack so as to achieve short per-packet latencies. Our rigorous measurements on off-the-shelf conventional networking and computing hardware demonstrate that CALVIN achieves round-trip times from a MEC ingress point via two elementary forwarding VNFs (one in kernel space and one in user space) and a MEC server to a MEC egress point on the order of 0.32 ms. Our measurements also indicate that MEC network coding and encryption are feasible for small 256 byte packets with an MEC latency budget of 0.35 ms; whereas, large 1400 byte packets can complete the network coding, but not the encryption within the 0.35 ms.
96 citations
TL;DR: A Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing (PAFLM) is proposed, which can allow multiple edge nodes to achieve more efficient federated learning without sharing their private data.
Abstract: In the traditional cloud architecture, data needs to be uploaded to the cloud for processing, bringing delays in transmission and response. Edge network emerges as the times require. Data processing on the edge nodes can reduce the delay of data transmission and improve the response speed. In recent years, the need for artificial intelligence of edge network has been proposed. However, the data of a single, individual edge node is limited and does not satisfy the conditions of machine learning. Therefore, performing edge network machine learning under the premise of data confidentiality became a research hotspot. This paper proposes a Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing (PAFLM), which can allow multiple edge nodes to achieve more efficient federated learning without sharing their private data. Compared with the traditional distributed learning, the proposed method compresses the communications between nodes and parameter server during the training process without affecting the accuracy. Moreover, it allows the node to join or quit in any process of learning, which can be suitable to the scene with highly mobile edge devices.
96 citations