关键词： 5G   computation offloading   game slicing   millimeter wave   mobile edge computing   potential game   vehicular network  

摘要： Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game's real-time convergence to an NE, reveal the NE's near-optimal performance, and present the efficacy of the proposed game slicing.

关键词： Availability-aware   service function chain   mobile edge computing  

摘要： With the advent of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC), outsourcing network functions (NFs) to the MEC is becoming popular among network service providers (NSPs), since it brings the scalability and flexibility for NF deployment and maintenance. Each user's request will go through a service function chain (SFC), which consists of several virtual network functions (VNFs, software substitutions for traditional hardware-based middleboxes) in a specific order, and then get a response. Unlike conventional hardware-based middleboxes, VNFs are not very reliable due to potential software faults and host malfunctions. Thus, a sensible way is to add redundancy for the primary VNFs of an SFC to enhance its availability. Nevertheless, which MEC node to place each VNF, and how many backup instances are enough to ensure the availability requirement of each SFC? These issues have not yet been resolved. In this article, we present the availability-aware provision of SFC (APoS) in the MEC environment with the primary goal of maximizing the number of served requests while meeting the requirements and reliability expectations of SFCs. For the APoS, we have primarily addressed the following two fundamental challenges: (i) First, how to efficiently map these primary and backup VNFs to meet the availability requirements of SFCs? At this point, we formulate it as an integer nonlinear programming (INLP) under the limitation of each MEC node's resources. This issue is NP-hard, and a novel binary N-back search method is proposed to derive the optimal solution for the primary and backup VNFs mapping;(ii) Second, how can we reduce the latency for users to access their desired SFCs? Then, we investigate how to minimize the average delay for all requests in each time slot. To solve this problem, we design an online service switching (OSS) method, which jointly considers the queuing delay, communication delay, and switching delay. It achieves the optimal solution

关键词： Mobile edge computing   Task partitioning   Computation offloading   Resource allocation   Deep reinforcement learning  

摘要： The continual development of mobile edge computing efficiently solves the problem that mobile devices are unable to handle computation-intensive tasks due to their computation capacity and battery restrictions. In this paper, we consider mobile awareness and dynamic battery charging in a multi-user and multi-server mobile edge computing system, where various tasks are generated successively on the user devices. Servers act as learning agents and collaborate with user devices to develop task partitioning and computation resource allocation strategies. With the purpose of decreasing task failure rate and improving system utility in the long term, which is closely related to latency, energy consumption, and server cost, optimal strategies are demanded by the system. We model the joint optimization problem as a multi-agent Markov decision process game. And a deep reinforcement learning method based on the multi-agent deep deterministic policy gradient algorithm is proposed, which employs neural networks and works in a centralized training and decentralized execution manner to optimize the strategies. Finally, simulation results demonstrate the effectiveness of our proposed algorithm in terms of reducing task failure rate and improving system utility.

关键词： Mobile edge computing   mmWave communication system   delay minimisation   Red Deer Algorithm  

摘要： Mobile edge computing (MEC)-oriented solutions are essential for various 5G wireless communication systems. It is considered a key technology for future communication systems because of its capability for fulfilling a broad variety of necessities of the developing wireless terminals in the form of Intelligent Vehicles, augmented reality and virtual reality devices like huge computation, low latency and high data rate. Further, the resource package has made the research on mobile data offloading. One probable novel spectrum in the subsequent generation networks is the millimetre wave (mmWave) communication systems. It attains important attention because of its high rate. This research work, this area focuses on the delay of minimisation strategy in mmWave MEC by jointly optimising the hybrid beam-forming and also resource allocation. Here, the utilisation of a well-performing Red Deer Algorithm (RDA) is the ultimate aim of the suggested model that intends to optimise the analogue beam-forming vectors at the users, the analogue and digital beam-forming matrices at the Base Station (BS), the computation task offloading ratios and resource allocation at the MEC server. Here, the minimisation of the delay or latency is attained. The comparative analysis of the proposed model over the other models demonstrates the superiority of the proposed algorithm in assisting the mmWave MEC system.

关键词： Mobile edge computing   task offloading   safety-criticality   workflow applications  

摘要： As the fundamental mechanism in mobile edge computing (MEC), task offloading strategy is of great significance to the quality of computing services provided by MEC systems. When coping with workflow applications, the precedence relations among tasks increase the difficulty in developing task offloading strategies. This paper studies the problem of safety-critical task offloading for workflow applications in a MEC environment. Considering the precedence constraints on workflow tasks and the overhead of security services, we formulate the safety-critical workflow offloading model with the objective of jointly optimizing the total completion time and energy consumption. By using a task sequence to represent a feasible solution to the optimization model, we introduce a family of heuristics to solve the safety-critical workflow offloading problem under precedence constraints upon workflow tasks. Depending on whether the offloading solution satisfies the precedence relations among workflow tasks, task sequences can be classified into two categories, i.e., precedence-aware and precedence-unaware offloading solutions. With the satisfaction of precedence constraints, a family of heuristics by using a precedence-aware strategy and a precedence-unaware strategy is designed to offload safety-critical workflow tasks. Given an offloading sequence and the operating conditions of MEC servers, the heuristic algorithms select the currently best MEC server to offload workflow tasks. Experimental results justify the performance of the proposed algorithms in solving the safety-critical workflow offloading problem under precedence constraints.

关键词： Mobile Edge Computing   Internet of Things   Offloading decision   Resource allocation   Delay   Energy consumption  

摘要： With the growing popularity of Internet of Things (IoT), Mobile Edge Computing (MEC) has emerged for reducing the heavy workload at the multi-cloud core network by deploying computing and storage resources at the edge of network close to users. In IoT, services are data-intensive and event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges in the IoT scenario with service dependencies. To this end, this paper proposes an intelligent approach for minimizing latency and energy consumption which jointly considers the task scheduling and resource allocation for dependent IoT services in MEC. Specifically, we establish the system model, communication model as well as computing model for performance evaluation by fully considering the dependent relationships among services, and an optimization problem is proposed for minimizing the delay and energy consumption simultaneously. Then, we design a layered scheme to deal with the service dependencies, and present detailed algorithms to intelligently obtain optimal task scheduling and resource allocation policies. Finally, simulation experiments are carried out to validate the effectiveness of the proposed scheme.

关键词： Mobile edge computing   Non -orthogonal multiple   Successful computation   Genetic algorithm   Tactile IoT   Multiuser   Best user selection   access   probability   Non-dominated Sorting Genetic Algorithm   III  

摘要： Mobile edge computing (MEC) is an essential technique in next-generation networks to serve ultra-low latency and computation-intensity applications. At the same time, nonorthogonal multiple access (NOMA) is a technique to help multi-user service, saving energy and increasing spectrum efficiency. In this study, we investigate the NOMA MEC-based wireless Tactile Internet of Things (IoT) network and propose the optimization algorithms for system and users perfor-mance: We propose a network model consisting of a MEC server at the access point that supports computation for two sensor clusters in the Tactile IoT environment. We analyze the performance of the system and cluster heads (CHs) using the successful computation probability (SCP). Asymptotic SCP at high SNRs was analyzed and compared by us to give a better view of the system's behavior. Then, we maximize the SCP of the proposed system and simultaneously maximize the SCP of the CHs to clarify the performance trade-off problem in the NOMA MEC network by proposing low -complexity meta-heuristic algorithms. Monte-Carlo simulation results show that our proposed approach can significantly improve system performance by up to 30% compared to OMA tradi-tional methods.(c) 2023 Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Mobile edge computing  

摘要： In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system’s average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is expected to play a significant role in the development of 6G networks, as new applications such as cooperative driving and eXtended Reality (XR) require both communication and computational resources from the network edge. However, the limited capabilities of edge servers may be strained to perform complex computational tasks within strict latency bounds for multiple clients. In these contexts, both maintaining a low average Age of Information (AoI) and guaranteeing a low Peak AoI (PAoI) even in the worst case may have significant user experience and safety implications. In this work, we investigate a theoretical model of a MEC server, deriving the expected AoI and the PAoI and latency distributions under the First In First Out (FIFO) and Generalized Processor Sharing (GPS) resource allocation policies. We consider both synchronized and unsynchronized systems, and draw insights on the robust design of resource allocation policies from the analytical results. © 2023, CC BY.

关键词： Task analysis   Servers   Resource management   Bandwidth   Quality of experience   Energy consumption   Delays   Task offloading   mobile edge computing   energy-constrained edge   Lyapunov optimization   game theory  

摘要： We consider the problem of task offloading and resource allocation in mobile edge computing (MEC). To maintain satisfactory quality of experience (QoE) of end-users, mobile devices (MDs) may offload their tasks to edge servers based on the allocated computation (e.g., CPU/GPU cycles and storage) and wireless resources (e.g., bandwidth). However, these resources could not be effectively utilized unless an encouraging resource allocation scheme can be proposed. What's worse, task offloading incurs additional MEC energy consumption, which inevitably violate the long-term MEC energy budget. Considering these two challenges, we propose an online joint offloading and resource allocation (JORA) framework under the long-term MEC energy constraint, aiming at guaranteeing the end-users' QoE. To achieve this, we leverage Lyapunov optimization to exploit the optimality of the long-term QoE maximization problem. By constructing an energy deficit queue to guide energy consumption, the problem can be solved in a real-time manner. On this basis, we propose online JORA methods in both centralized and distributed manners. Furthermore, we prove that our proposed methods enable the achievement of the close-to-optimal performance while satisfying the long-term MEC energy constraint. In addition, we conduct extensive simulations and the results show superiority in performance over other methods.