关键词： Video content placement   mobile edge computing   online optimization   multi-agent reinforcement learning   FUTURE  

摘要： In the traditional video streaming service paradigm, content providers typically provision the requested video content to viewers through a central content delivery network (CDN). However, remote viewers usually experience long video streaming delay due to uncertain wide area network delay, which severely affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

关键词： Mobile edge computing   distributed edge caching   mean-field game theory   dynamic requirements   optimal control   SMALL-CELL   5G   INTERFERENCE   NETWORKS   OPTIMIZATION  

摘要： Mobile edge computing (MEC) can use wireless access network (RAN) to provide users with nearby information technology (IT) services and cloud computing functions, which creates a high-performance and low latency service environment. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission. However, the time-varying and dynamic of the content requests may lead to the base station to cache the useless contents. In this paper, we study a distributed caching optimization problem in edge networks (ENs) with the spatio-temporal requirements. In the considered ENs, the cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the total cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original optimization problem is transformed into a mean field game (MFG). We propose a distributed caching iterative control algorithm that decouples the information interaction between the general SBS and others through the mean field distribution. In addition, we obtain the optimal edge caching control strategy, while the existence and uniqueness of the mean field equilibrium (MFE) can also be guaranteed. Simulation results demonstrate that our proposed caching control algorithm can average reduce 27.12% storage cost and achieve better performance than other existing schemes.

关键词： Mobile edge computing   D2D communication   parallel task offloading   content caching   multi-objective optimization   RESOURCE-ALLOCATION   EDGE-CLOUD   OPTIMIZATION   DELIVERY  

摘要： In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

关键词： Communication security   Mobile edge computing   Data offloading   Artificial intelligence   UAV communication   RESOURCE-ALLOCATION  

摘要： To solve the problem that mobile users cannot handle all data tasks by themselves due to the huge amount of data at present, this paper proposes a four-layer model of efficient and secure multiuser multitask computing offload. Since the user needs to offload the data to the server for processing through the wireless communication link, and users have mobility, it is difficult to achieve dynamic updates of user status and data information based on traditional cloud computing data processing methods, and it is difficult to ensure security during data unloading. In this paper, we studied how to realize the efficient and safe transmission of mobile users' data tasks when deploying Mobile Edge Computing (MEC) servers with artificial intelligence to assist in data processing on Unmanned Aerial vehicles (UAVs). First of all, to ensure the efficient use of computing resources, we use compression algorithms to reduce transmission overhead. Secondly, to enhance the security of the data trans-mission process, we have designed a security layer in the system model, which mainly uses secure multiparty technology to encrypt sensitive data, and a data transmission algorithm based on artificial intelligence is designed to assure the efficient and secure transmission of data in the network. Finally, through the analysis of the simulation results, we can know that the proposed strategy in this study has better performance than other existing strategies.

关键词： Internet of things   Mobile edge computing   Cloud   Optimization   Fog computing   Task scheduling   WORKLOAD ALLOCATION   INTERNET   ENERGY  

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 methods.The 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)   NONORTHOGONAL MULTIPLE-ACCESS   SUM-RATE MAXIMIZATION   MULTICELL NETWORKS   RESOURCE-ALLOCATION   ASSOCIATION   SYSTEMS  

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 themselves.Mobile edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to MEC.In this paper,we study the problem of dynamic task offloading for digital twin-empowered MEC.Digital twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT devices.We formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ESs.Then,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve it.Comparative experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

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   ALLOCATION   INTERNET   AUCTION   MOEA/D   IOT  

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.