关键词： Mobile Edge Computing   Digital Twin   Service migration   Digital Twin edge network   User mobility   Resource allocation  

摘要： Mobile Edge Computing (MEC) is emerging as one of the key technologies to process massive amount of data at the edge of the network for upcoming 6G networks. In the paradigm of MEC, users can offload the computation -intensive and delay-sensitive tasks to the edge servers, since the capabilities of User Equipment (UE) cannot be not sufficient to meet different quality of service (QoS) requirements. However, due to the limited coverage of edge servers, users can move between multiple edge servers. In such cases, service migration is complicated which arises the challenge of where and when to migrate the service dynamically to maintain acceptable QoS. In addition, high uncertainty of the user mobility and different QoS requirements of the services are challenging. Instead of assigning each user to the nearest edge server, we propose a Digital Twin (DT)-assisted service migration approach to minimize the task completion time. The contributions in this paper are threefold: First, a DT edge network architecture is presented that uses the real-time and historical data to determine service migration strategy. Second, the DT classifies the computing tasks as delay-sensitive task and delay -insensitive tasks to meet the QoS requirements. Third, the DT predicts the mobility of users in the future based on the Hidden Markov Model (HMM) and studies the impact of reducing the cost of service migration. Thus, we propose an optimization algorithm to find optimal resource allocation at each edge server. The simulation results shows that our DT-assisted service migration model can achieve less delay and service migration rate compared with the traditional methods.

关键词： ant colony algorithm   mobile edge computing   resource scheduling   sapce information network   task decomposition  

摘要： With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.

关键词： mobile edge computing   mobile servers   task assignment   energy use balancing  

摘要： Mobile edge computing has been an important computing paradigm for providing delay-sensitive and computation-intensive services to mobile users. In this paper, we study the problem of the joint optimization of task assignment and energy management in a mobile-server-assisted edge computing network, where mobile servers can provide assisted task offloading services on behalf of the fixed servers at the network edge. The design objective is to minimize the system delay. As far as we know, our paper presents the first work that improves the quality of service of the whole system from a long-term aspect by prolonging the operational time of assisted mobile servers. We formulate the system delay minimization problem as a mixed-integer programming (MIP) problem. Due to the NP-hardness of this problem, we propose a dynamic energy criticality avoidance-based delay minimization ant colony algorithm (EACO), which strives for a balance between delay minimization for offloaded tasks and operational time maximization for mobile servers. We present a detailed algorithm design and deduce its computational complexity. We conduct extensive simulations, and the results demonstrate the high performance of the proposed algorithm compared to the benchmark algorithms.

关键词： IRS   mobile edge computing   reinforcement learning   deep deterministic policy gradient  

摘要： By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly tune the communication environment and improve the system energy efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for the IRS-assisted NOMA MEC system. We first formulate a mixed integer energy efficiency maximization problem with system queue stability constraint. We then propose the Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (LMIDDPG) algorithm which is based on the centralized reinforcement learning (RL) framework. To be specific, we design the mixed integer action space mapping which contains both continuous mapping and integer mapping. Moreover, the award function is defined as the upper-bound of the Lyapunov drift-plus-penalty function. To enable end devices (EDs) to choose actions independently at the execution stage, we further propose the Heterogeneous Multi-agent LMIDDPG (HMA-LMIDDPG) algorithm based on distributed RL framework with homogeneous EDs and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy efficiency performance to the benchmark algorithms while maintaining the queue stability. Specially, the distributed structure HMA-LMIDDPG can acquire more energy efficiency gain than the centralized structure LMIDDPG.

关键词： Mobile edge computing   radio access network   micro-architecture   mobile edge co-located workloads  

摘要： Nowadays, virtual Radio Access Network (vRAN) plays a vital role in today's mobile edge system for its better support for latency-sensitive applications. However, our characterization of vRAN on modern processors depicts a frustrating picture of Single-Instruction Multi-Data (SIMD) acceleration. Specifically, the existing data arrangement processes cannot efficiently utilize the ports in modern processors, which leads to high backend bound and fails to saturate the memory bandwidth between registers and the L1 cache. To tackle the issue, we thoroughly examine the state-of-the-art CPU architecture and observe the idle ports which could be utilized by the process. Motivated by this observation, we propose an "Arithmetic Ports Consciousness Mechanism" (APCM) utilizing these idle ports to eliminate the backend bound and saturate the memory bandwidth. The APCM decreases the data arrangement's backend bound from 45% to 3% and promotes its memory bandwidth utilization by 4X-16X. Moreover, we illustrate that the APCM can be utilized to promote the performance of typical mobile edge applications such as network routing, image processing, and AI applications. The CPU time of the data arrangement process time of the selected typical mobile edge applications can be reduced by 55$\%$% - 95$\%$% when utilizing the proposed mechanism.

关键词： D2D   dependency task offloading   energy consumption   mobile edge computing   network latency  

摘要： With the advent of 5G, Mobile Edge Computing (MEC), a promising computing paradigm sits closer to users than cloud computing, is being broadly used in various Internet of Things (IoT) applications, and achieve high-quality user experience. Task offloading, as a critical research issue in MEC, is playing an important role in optimizing computational resources and management. However, many tasks are executed dependent on the computational results of other tasks. Moreover, in the case of offloading tasks with other devices, it is often required to consider the success rate of offloading, since not all users are willing to lend their mobile devices to others for task execution. To address this challenge, by taking social relationships between users into account, this paper intends to combine computational resources of local devices and edge clouds and provide more flexible offloading and execution solutions, for achieving the efficient offloading of dependent tasks with the joint consideration of network latency and energy consumption. This paper develops a dependent task offloading strategy based on Bipartite Graph Matching. Extensive simulations are conducted for validating the effectiveness of our proposed strategy. Experimental results demonstrate that our proposed strategy can significantly minimize the overhead compared with other baseline strategies. In particular, the overhead is reduced 8.2%, compared with the strategy which consider the Device-to-Device (D2D) offloading only.

关键词： space-ground-air collaborative network   mobile edge computing   drone communication   non-orthogonal multiple access (NOMA)   task offloading and resource allocation  

摘要： The space-air-ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space-air-ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices' tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave-convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

关键词： Mission-critical communications   teleoperation   real-time systems   telerobotics   human-machine interaction   mobile edge computing   low-latency high-reliability  

摘要： The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of end-to-end latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: an HO, a robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component is used to analyze the closed-loop latency and optimize the compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, including a large class of systems whose latency budget consists of many components.

关键词： Trust   Internet of Things   mobile edge computing   machine learning   online learning  

摘要： Mobile Edge Computing (MEC)-based Internet of Things (IoT) systems generate trust information in a real-time and distributed manner. Predicting trustworthiness of IoT services in such an MEC environment requires new prediction strategies that cater for the aforementioned characteristics of trust information. More importantly, it is imperative to investigate how the real-time trust information could be effectively integrated into trust prediction strategies in order to capture the ever-evolving nature of trustworthiness of IoT services. In turn, such a strategy allows IoT service consumers to derive more relevant and accurate trust-based decisions. To that end, our work models trust prediction in MEC-based IoT systems as an online regularized finite-sum problem in a distributed MEC environment with a given MEC topology. We then adopt the Online Alternating Direction Method (OADM) to effectively train trust prediction models in parallel over the distributed MEC environment. OADM allows splitting the aforementioned finite-sum problem into multiple sub-problems that correspond to different local MEC environments. These sub-problems can then be solved iteratively within each local MEC environment by using the local trust data therein. This can avoid the movement of data across the core networks of mobile network providers. Experiments on real-world and synthetic datasets demonstrate the effectiveness and scalability of the proposed method.

关键词： Collaborative computation offloading   Mobile edge computing   DQN   6G   Software-defined networking   Network congestion  

摘要： The implementation of mobile edge computing (MEC) and software-defined networking (SDN) over sixth-generation networks is a driving force in the future of cloud computing. It holds significant promise in addressing smart device (SD) resources and battery life limitations. To deal with the variety and resource constraints of SDs while making better use of network infrastructure, collaborative offloading has emerged as a viable technique for improving the ability to schedule independent tasks while alleviating the burden of restricted computation resources and network congestion. Network congestion is a common issue when a network node or link carries more data than it can manage. Existing works frequently overlook the impact of insufficient edge server capacity and network congestion. This paper primarily focuses on an SDN-powered MEC network that utilizes a full offloading policy, which completely offloads all tasks from the SD to the edge server and adopts a collaborative offloading of the MEC network when it is overloaded. The problem also takes into consideration when and to whom to offload the task. To bridge this gap, we first implement a collaborative offloading scheme among MEC servers based on the edge server's resources and neighbors' status to alleviate network congestion. It takes advantage of the computing capacities of edge servers deployed at the network's edge and the SDN controller's global view of the entire network. Then we devise a Deep Q-Network methodology to achieve near-optimal performance, and minimize the total execution time concerning deadline constraints. The experiments reveal that our proposed task scheduling of collaborative computation offloading algorithm can significantly minimize the total execution time more than the existing schemes.