关键词： Mobile edge computing  

摘要： This paper addresses the Edge User Allocation (EUA) problem in edge computing, where the appropriate mapping from users to edge servers (ESs) is crucial for optimizing performance metrics. Game theory is one of the powerful tools used in edge computing for user allocation to edge resources and task offloading. However, this approach takes longer to converge to Pure Nash Equilibrium (PNE), which is called a stable optimal solution. In this paper, we propose a grouping technique for ESs, enabling parallel execution of game-theoretic approaches to achieve faster convergence at the PNE. Our contributions include the grouping method, the introduction of parallel Best Response (BR) dynamics for rapid convergence, and proof that the parallel BR dynamics will eventually halt at PNE. We also provide empirical evidence demonstrating its efficiency compared to traditional BR dynamics. This research enhances the scalability and effectiveness of game-theoretic approaches in resolving the EUA problem, offering practical solutions for edge computing scenarios. © 2024 IEEE.

关键词： Mobile edge computing  

摘要： In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems. © 2007-2012 IEEE.

关键词： Mobile edge computing  

摘要： Edge computing (EC), positioned near end devices, holds significant potential for delivering low-latency, energy-efficient, and secure services. This makes it a crucial component of the Internet of Things (IoT). However, the increasing number of IoT devices and emerging services place tremendous pressure on edge servers (ESs). To better handle dynamically arriving heterogeneous tasks, ESs and IoT devices with idle resources can collaborate in processing tasks. Considering the selfishness and heterogeneity of IoT devices and ESs, we propose an incentive-driven multi-level task allocation framework. Specifically, we categorize IoT devices into task IoT devices (TDs), which generate tasks, and auxiliary IoT devices (ADs), which have idle resources. We use a bargaining game to determine the initial offloading decision and the payment fee for each TD, as well as a double auction to incentivize ADs to participate in task processing. Additionally, we develop a priority-based inter-cell task scheduling algorithm to address the uneven distribution of user tasks across different cells. Finally, we theoretically analyze the performance of the proposed framework. Simulation results demonstrate that our proposed framework outperforms benchmark methods. © 2014 IEEE.

关键词： Mobile edge computing  

摘要： To meet the stringent requirement of edge intelligence applications, resource-constrained devices can offload their task to nearby resource-rich devices. Resource awareness, as a prime prerequisite for offloading decision-making, is critical for achieving efficient collaborative computation performance. Although major works have explored computation offloading in dynamic edge environments, the impact of fresh resource information perception has not been formally investigated. To bridge the gap, we design a cost-aware edge resource probing (CERP) framework for infrastructure-free edge computing, where a task device self-organizes its resource probing to enable informed computation offloading. We first formulate the joint optimization of device probing and offloading as a multi-stage optimal stopping problem and derive a multi-threshold-based optimal strategy with theoretical guarantees. Accordingly, we devise a data-driven layered learning mechanism to handle more complex real-world scenarios. The layered learning enables the task device to adaptively learn the optimal probing sequence and decision thresholds on the fly, aiming to strike a good balance between the gain of choosing the best edge device and the accumulated cost of deep resource probing. To further boost its learning efficiency, we replace the ϵ-greedy method with a tailored UCB-based adaptive exploration scheme in layered learning, thus better navigating the exploration and exploitation trade-off during probing processes. Finally, we conduct a thorough performance evaluation of the proposed CERP schemes using both extensive numerical simulations and realistic system prototype implementation, which demonstrate the superior performance of CERP in diverse application scenarios. © 2008-2012 IEEE.

关键词： Mobile edge computing  

摘要： The widespread adoption of the Internet of Things (IoT) has increased demand for task processing via mobile edge computing (MEC). In this study, we designed a directed acyclic graph (DAG) task offloading workflow in MEC. Traditional task offloading often does not simultaneously take into account task upload delay and task communication delay, failing to accurately reflect real-world issues. The constraints between task execution delay, upload delay and communication delay were introduced to model system response time and energy consumption for optimization. To satisfy task dependencies, the edge rank_u sorting (ERS) algorithm is used to generate specific offloading queues. A federated deep q-network (FDQN) algorithm addresses the offloading issue. It is different from the traditional approach of uploading task information data to the edge and facing data privacy risks. FDQN deploies the model locally and only collects model parameters for aggregation to update the local model. The algorithm improves the performance and stability of the model while protecting user privacy. To automatically tune hyperparameters for multiple devices, we used the tree of parzen estimators (TPE) algorithm, and named the whole process federated DQN with automated hyperparameter optimization (FDAHO). Experimental results show that FDAHO outperforms other algorithms in scenarios of different task number, task types, and user numbers, with consideration of benchmarks. © 2008-2012 IEEE.

关键词： Internet of Vehicles   Delays   Servers   Routing   Handover   Information-centric networking   Smart cities   Edge-based content provisioning   information-centric networking   mobile edge computing   point of attachment  

摘要： Over the years, the Internet of Vehicles (IoV) and Information-Centric Networking (ICN) has gained significant popularity due to emergence of content provisioning in smart environments. The content provisioning in mobile and seamless environments is challenging due to frequent change in Point of Attachement (PoA) or handovers across the networks. In Edge supported IoV based ICN, the consumer's interest packets are not satisfied until the producer re-connects to the new PoA and informs its current location to the next content router. The idea is to satisfy the interest packets by providing the requested data content to the consumer node at its current PoA. The existing ICN architectures without edge support within the zone do not handle and support mobility aware content provisioning to the consumers in the IoV environment. In this paper, we propose Mobility-Aware Content Provisioning in Edge-based content-centric Internet of Vehicles (MACPE). That manages the network in zones with content routers for up-to-date content and producer mobility information. The simulation results show that the proposed MACPE scheme outperforms GeoZone scheme in terms of benchmark parameters. i.e. packet delivery ratio, average end-to-end delay, handover delay, average hop count and throughput during producer mobility scenarios in intra and inter-edge-based zones.

关键词： Computation offloading   Edge orchestration   EdgeCloudSim   Internet of things   Mobile edge computing   Offloading decision   Processing optimization  

摘要： The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version. © 2024 Institute of Advanced Engineering and Science. All rights reserved.

关键词： Mobile edge computing  

摘要： E dge computing is a distributed computing paradigm that moves processing and data closer to the consumer. The main benefi ts of edge computing include decreased latency, higher bandwidth availability, and fi ne-grained control of quality of service (QoS).

关键词： mobile edge computing   MEC   processor sharing server   ultra low latency communication augmented reality  

摘要： In wireless communication, ultra low-latency communications (ULLC) services offer short packets that may coincide alongside enhanced mobile broadband (eMBB) services which send lengthy packets. In a mobile edge computing architecture with eMBB and URLLC services, we explore how to evaluate latency and enhance shorter packet offloading methods. Using eMBB and URLLC functions, we examine how to evaluate latency and enhance shorter packet offloading methods in mobile edge computing (MEC) approach. In the MEC system, a server called processor sharing (PS) is utilised to reduce computation delay for short packets by distributing the server's whole processing power equally to all packets. The server ignores long packets in favour of shorter ones. With a small packet size, a closed-form formula for the complementary latency distribution function may be developed. To minimise the short packet's end-to-end (E2E) latency, offloading probabilities are adjusted based on the dependable demand. In tests with short and long packets, the processor-sharing server outperforms two types of first-come, first-serve servers.

关键词： clustering   Internet of Vehicles   machine learning   mobile edge computing   multi-hierarchical caching   Thompson sampling learning  

摘要： This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments. © 2024 National Institute of Telecommunications. All rights reserved.