关键词： Internet of things   Mobile edge computing   Cloud   Optimization   Fog computing   Task scheduling  

摘要： Due to the evolution of Internet of Things (IoT) paradigm, the number of devices is growing day by day which arises the stringent requirements for various communications. The standard cloud computing model is not capable of efficiently hosting IoT tasks due to the high latency associated with it. Hence, this work utilizes the mobile edge computing and fog concept to process the input tasks independent of the cloud layer. Generally, the high computation costs and energy consumption resulted in the applications such as power-hungry and computation-intensive, which become massive challenges for an IoT device. Motivated by the previous research, we mainly plan to investigate the resource allocation issues that arise in the MEC-enabled IoT-Fog-cloud architecture. We propose a Poplar Optimization algorithm (POA) based on Attention 1DCNN-LSTM architecture (POA-A1DCNN-LSTM) for improving the total utility of the MEC servers by optimizing the energy consumption and task delay. Initially, the POA algorithm is implemented for cluster head (optimal fog node) selection using the node degree, node distance, and residual energy. Next, the A1DCNN-LSTM architecture is employed for task offloading by selecting the fog node with minimal task length and processing delay. The performance of the proposed method is validated by average latency, user satisfaction, network lifetime, energy consumption, average time delay, and normalized system utility. The experimentation results revealed that the proposed method attained better effectiveness in different metrics by achieving 1.5 ms, 0.65, 60 s, 0.45 j, 385 ms, and 0.98 compared to state-of-the-art *** experimentation outcomes demonstrate the effectiveness of the proposed POA-A1DCNN-LSTM architecture to decrease task completion delay and offer effective task scheduling as well as resource allocation performances in the MEC-enabled IoT-Fog-cloud network.

关键词： Discrete power allocation   online user allocation   mobile edge computing   non-orthogonal multiple access (NOMA)  

摘要： This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system's energy efficiency. To achieve this objective while guaranteeing users' data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem.

关键词： deep reinforcement learning   digital twin   Internet of Things   mobile edge computing  

摘要： Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by *** edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to *** this paper,we study the problem of dynamic task offloading for digital twin-empowered *** twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT *** formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ***,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve *** experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

关键词： Sensors   Internet of Things   Resource management   Artificial neural networks   Array signal processing   Computational efficiency   Task analysis   Reconfigurable intelligent surfaces   Multi-access edge computing   Symbiotic radio   reconfigurable intelligent surface   mobile edge computing   data sensing   alternating optimization  

摘要： In the article, we investigate the coordination process of sensing and computation offloading in a reconfigurable intelligent surface (RIS)-aided base station (BS)-centric symbiotic radio (SR) systems. Specifically, the Internet-of-Things (IoT) devices first sense data from environment and then tackle the data locally or offload the data to BS for remote computing, while RISs are leveraged to enhance the quality of blocked channels and also act as IoT devices to transmit its sensed data. To explore the mechanism of cooperative sensing and computation offloading in this system, we aim at maximizing the total completed sensed bits of all users and RISs by jointly optimizing the time allocation parameter, the passive beamforming at each RIS, the transmit beamforming at BS, and the energy partition parameters for all users subject to the size of sensed data, energy supply and given time cycle. The formulated nonconvex problem is tightly coupled by the time allocation parameter and involves the mathematical expectations, which cannot be solved straightly. We use Monte Carlo and fractional programming methods to transform the nonconvex objective function and then propose an alternating optimization-based algorithm to find an approximate solution with guaranteed convergence. Numerical results show that the RIS-aided SR system outperforms other benchmarks in sensing. Furthermore, with the aid of RIS, the channel and system performance can be significantly improved.

关键词： 6G edge networks   mobile edge computing   survivability guarantee   node-overload protection   traffic prediction   task offloading  

摘要： With the development of sixth-generation (6G) mobile networks, the rise of emerging intelligent services has led to a huge increase in traffic. As an important technology to support the development of 6G, mobile edge computing (MEC) effectively meets the ultra-low latency requirements of most emerging services. However, due to the limited processing capacity of edge nodes, overload on any edge node will cause service degradation, interruption, and even node failure, weakening the advantages of MEC and reducing the survivability of the whole network. In this paper, we propose a task offloading scheme based on traffic prediction for node-overload protection to ensure the survivability of the 6G edge networks. We transformed the network survivability guarantee problem into a task offloading problem under the constraint of future available resources based on traffic prediction and developed a particle swarm optimization algorithm based on policy gradient (PSO-PG) to jointly optimize offloading decisions, routing, and computing resource allocation. Simulations verify the effectiveness of our proposed scheme and guarantee the survivability of 6G edge networks. Meanwhile, evaluation in multiple scenarios with different node scales has verified the wide applicability of our work.

关键词： Mobile edge computing   Service placement   Resource allocation   State sampling   Approximation  

摘要： The rise of Mobile Edge Computing (MEC) has brought us its enormous potential and value in mobile service applications. By pushing computing and storage resources from the cloud to the network edge, it reduces transmission latency, and supports applications that require low latency, such as virtual reality and video analytics IoT services. However, many existing works only consider the problem of service placement in MEC, and the problem of computing resource allocation has received less attention. This paper focuses on the joint optimization of Service Placement and computational Resource Allocation (SPRA) in the MEC environment, with the goal of minimizing the total cost of service latency, communication latency, and service migration. For offline cases, we propose an optimal algorithm, D-SPA , based on dynamic programming and an improved algorithm, S-SPA, based on state sampling, which effectively alleviates the state explosion problem in D-SPA. In the online case, due to the unpredictability of future environmental information, we propose the online greedy algorithm OGA, and theoretically show the approximate ratio of OGA. Extensive experiments show that our algorithm is more efficient than other baseline algorithms, and it reduces the total cost by 28.6% on average.

关键词： Mobile edge computing   Deep reinforcement learning   Computation offloading   Resource allocation   Multi-branch architecture  

摘要： Mobile Edge Computing (MEC) is a promising computing paradigm in the context of 5G networks, as it enables the migration of workloads from User Equipments (UEs) to nearby MEC servers, thereby providing additional computing resources to UEs. In this paper, we propose a joint optimization approach to offloading decisions and resource allocation in a multi-user and multi-server MEC system, which operates in a time-varying environment. Our objective is to minimize the average task latency and discard rate under the constraints of latency and limited computing resources of the server. While traditional optimization methods have been used to solve computational offloading problems in static environments, these methods are not suitable for time-varying systems. Deep reinforcement learning can be an effective method for solving optimization problems in time-varying environments, as it can be used to adjust strategies in real-time in response to changes in the environment. However, the increased number of UEs in the system leads to a combinatorial increase in the number of possible actions, making it difficult for the algorithm to learn. To address this issue, we propose a multi-branch network based Deep Q Network (DQN) algorithm called Branch Deep Q Network (BDQN), which modifies the action generation network into a multi-branch network structure, each branch generates one-dimensional action. This modification makes the number of network outputs increase linearly. Numerical results show that the BDQN algorithm outperforms other baseline algorithms in terms of average task latency and discard rate.

关键词： Dependent task offloading   Mobile edge computing   Multi-objective optimization   Evolutionary computation   Cloud-edge-end collaborative computing  

摘要： Due to the proliferation of applications such as virtual reality and online games with high real-time requirements, Mobile Edge Computing (MEC) has become a promising computing paradigm that can improve user experience and reduce the task offloading latency. The cloud-edge-end collaborative offloading further addresses the problem of insufficient computing resources of edge servers owing to large-scale computing-intensive applications in MEC. However, existing offloading solutions often ignore the important factor of economic cost, making it hard for these solutions to achieve a sustainable cloud-edge-end collaborative computation. To this end, this paper considers a multi-user multi-server, cloud-edge-end collaborative offloading scenario in the presence of dependent offloading tasks for the sake of maximizing rewards and minimizing latency. Each user issues a computing-intensive application consisting of multiple dependent tasks, which are offloaded collaboratively by various computational resources. With the goal of maximizing the yield of offloading for users and server providers, a multi-objective optimization problem of joint task offloading and execution rewards is studied. Technically, a multivariate multi-objective optimization problem with three objectives is modeled. An efficient multi-objective evolutionary optimization algorithm based on MOEA/D is then developed to solve the latency minimization and reward maximization problems. Extensive simulation results verify the effectiveness of the algorithm and illustrate that the proposed algorithm can significantly improve user offloading benefits. In addition, a scalability evaluations of our proposed algorithm is conducted for demonstrating its feasibility in large-scale task offloading scenarios.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： deep reinforcement learning   advanced asynchronous advantage actor-critic (A3C)   multi-agent system   mobile edge computing   cloud computing   computational offloading   energy efficiency  

摘要： Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user's equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method's effectiveness is further shown by comparing it to other benchmarks.

关键词： Servers   Resource management   Task analysis   Costs   Cloud computing   Delays   Minimization   Deep learning   Q-factor   Edge computing   Computation offloading   double deep Q-Network   mobile edge computing   resource allocation   service caching  

摘要： Mobile Edge Computing (MEC) provides cloud-like computing functionalities for mobile devices (MDs) by deploying servers at the edge of future 6G network. The cooperation among multi-servers and remote cloud offers low-latency services for MDs, meeting the demand of numerous delay-sensitive computation tasks. However, the limited storage capacity of edge servers makes it difficult to cache all kinds of services from the cloud simultaneously. Furthermore, the available bandwidth of communication links and the computation capacity of edge servers are insufficient for all MDs. In this paper, we consider a multi-server MEC scenario, where exists the competitions of multiple MDs for communication and computation resource, and various services for storage capacity. We investigate task offloading and resource allocation strategy in the three-tier MEC system, and explore the service caching strategy of the edge server. We aim at minimizing the cost of delay and energy consumption, and formulate the joint optimization problem as Markov Decision Process (MDP). Based on Double Deep Q-Network (DDQN) algorithm, the joint service caching, resource allocation and computation offloading scheme (SCRACO) is proposed to solve the problem. The simulation results demonstrate that the proposed scheme can effectively reduce total cost of the system compared with other three benchmarks.