关键词： 5G   6G   mobile edge computing   migration   offloading   quality of service  

摘要： Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability,availability,and ultra-low *** requirements of such networks are the main challenges that can be handled using a range of recent technologies,including multi-access edge computing(MEC),artificial intelligence(AI),millimeterwave communications(mmWave),and software-defined *** aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service(QoS).This article considers developing a complex MEC structure for fifth and sixth-generation(5G/6G)cellular ***,we propose a seamless migration technique for complex edge computing *** developed migration scheme enables services to adapt to the required load on the radio *** proposed algorithm is analyzed for various use cases,and a test bench has been developed to emulate the operator’s *** obtained results are introduced and discussed.

关键词： cost optimization   dual optimization   IoT system   mobile edge computing   relay selection   zero latency  

摘要： The evolution of IoT, 5G and 6G aims to provide almost zero latency. Computation tasks size is different for different users. A framework for task computation achieving almost zero latency for stochastic demand is a challenge. A relay-based D2D mobile edge computing (MEC) system is proposed. The idle device present in the networks is used as relay resources (RS). Mobile devices (MDs) communicate task to relay resources (RS) using D2D communication link. The RS perform computation and offloaded to edge server (ES). It aims to minimize total cost, energy expenditure and overall latency. Problem is formulated as mixed-integer nonlinear-constrained problem (MINCP). A three-step algorithm to optimize relay selection, power allocation and computation resource allocation is proposed. In the initial step optimal relay selection is obtained by the Kuhn-Munkres (KM) algorithm. In the next step, power allocation is obtained using Q-learning. In the last step, the main problem is converted into a cost optimization problem deciphered by the proposed algorithm. The substantial simulation results indicate the relay-based MEC system to achieve an astounding outcome in terms of latency, energy consumption and cost. Compared with other baseline methods, the proposed algorithm can achieve reduced energy consumption and cost for almost zero latency.

关键词： Mobile edge computing   Deep neural network (DNN)   DNN partitioning and offloading   Heterogeneous edge computing  

摘要： Mobile edge computing is conducive to artificial intelligence computing near terminals, in which Deep Neural Networks (DNNs) should be partitioned to allocate tasks partially to the edge for execution to reduce latency and save energy. Most of the existing studies assume that the tasks are of the same type or the computing resources of the server are the same. In real life, Mobile Devices (MDs) and Edge Servers (ESs) are heterogeneous in type and computing resources, it is challenging to find the optimal partition point for each DNN and offload it to an appropriate ES. To fill this gap, we propose a partitioning-and-offloading scheme for the heterogeneous tasks-server system to reduce the overall system latency and energy consumption on DNN inference. The scheme has four steps. First, it establishes a partitioning and task offloading model for adaptive DNN model. Second, to reduce the solution space, the scheme designs a Partition Point Retain (PPR) algorithm. After that, the scheme gives an Optimal Partition Point (OPP) Algorithm to find the optimal partition point with the minimum cost for each ES corresponding to each MD. Based on the partition points, an offloading of DNN tasks for each MD is presented to finish the whole scheme. Simulations show that the proposed scheme reduces the total cost by 77.9% and 59.9% on average compared to Only-Local and Only-Server respectively in the heterogeneous edge computing environment.

关键词： Mobile edge computing   D2D underlay communication   Price elasticity model   Log-log model   Task offloading   Delay tolerance  

摘要： The development of 5G/6G aims to provide high mobile computing with ultra-reliable low latency. The implementation of latency-sensitive computing applications is causing the overall cost management of the network to be a challenging issue. A Device-to-Device (D2D) underlay mobile edge computation offloading system has been developed to achieve these goals. This work intends to create a mobile edge computing (MEC)-D2D underlay system with cost optimization using the price elasticity log-log model ((PELM)-M-2). In this system, the task computation of each device can be partially offloaded to the edge server and nearby mobile helper. This work focuses on minimizing the computation cost of the network with computation and communication constraints. The optimization problem P1 is formulated as a mixed-integer nonlinear programming problem (MINLP). The problem P1 is split up into two sub-problems denoted by P2 and P3, respectively. The solution to P2 optimizes the task division ratio to meet delay tolerance. The solution to P3 minimizes energy consumption by using the price elasticity log-log model ((PELM)-M-2). Numerical results show that the proposed PEL2M better than MEC system with binary offloading (MECBO), MEC system with partial offloading (MECPO), Mobile edge computing and device-to-device underlay system with partial offloading (MECD2DUPO), Brute-force algorithm and Hungarian algorithm in terms of reduction in energy consumption by 72.71, 73.24, 66, 70.15, and 66.30%, and the latency is reduced by 88.4, 16.89, 9.5, 11.02, and 9%, respectively. The proposed algorithm may be applied to solve various latency-constrained applications in medical fields, vehicular communication, etc.

关键词： Path planning   Cloud computing   5G mobile communication   Quality of service   Task analysis   Resource management   Marine vehicles   Mechanism design   mobile edge computing   path planning   pricing   quality of service   service handoff  

摘要： Many mobile device applications require low end-to-end latency to edge computing infrastructure when offloading their computation tasks in order to achieve real-time perception and cognition for users. User mobility brings significant challenges in providing low-latency offloading due to the limited coverage area of cloudlets. Virtual machine (VM)/container handoff is a promising solution to seamlessly transfer services from one cloudlet to another to maintain low latency as users move. However, an inefficient path planning for the handoff can result in system congestion and consequently poor Quality of Service (QoS). The situation can even worsen by selfish users who intentionally lie about their true parameters to achieve better service at the cost of degrading the whole system's performance. To fill this research gap, we propose an Online Service Handoff Mechanism (OSHM) to provide an efficient path dynamically for transferring VM/container from the current serving cloudlet to a nearby cloudlet at the destination of a mobile user. Our proposed path planning algorithm is based on a label correction methodology, leading to polynomial time complexity. OSHM is accompanied by our proposed payment determination function to discourage misreporting of unknown parameters. We discuss the theoretical properties of our proposed mechanism in implementing a system equilibrium and ensuring truthfulness. We also perform a comprehensive assessment through extensive experiments which show the efficiency of OSHM in terms of workload, handoff time, consumed energy, and other metrics compared to several benchmarks. Experimental results show that OSHM outperforms other algorithms, reducing at least 61% in average workload, 33% in average handoff time, and 29% in average energy consumption.

关键词： Approximation algorithm   edge data caching   fairness   integer programming   mobile edge computing   optimization  

摘要： Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its $\mathcal {NP}$NP-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.

关键词： Asynchronous reinforcement learning   Mobile edge computing   Deep reinforcement learning   Online computing offloading  

摘要： The processing of large volumes of data sets unprecedented demands on the computing power of devices, and it is evident that resource-constrained mobile devices struggle to satisfy the need. As a distributed computing paradigm, edge computing can release mobile devices from computation-intensive tasks, reducing the strain and improving processing efficiency. Traditional offloading methods are less adaptable and do not work in some harsh settings. We simplify the problem to binary offloading decisions in this research and offer a new Asynchronous Update Reinforcement Learning-based Offloading (ARLO) algorithm. The method employs a distributed learning strategy, with five sub-networks and a central public network. Each sub-network has the same structure, as they interact with their environment to learn and update the public network. The sub-network pulls the parameters of the central public network every once in a while. Each sub-network has an experienced pool that minimizes data correlation and is particularly successful in preventing situations where the model falls into a local optimum solution. The main reason for using asynchronous multithreading is that it allows multiple threads to learn the strategy simultaneously, making the learning process faster. At the same time, when the model is trained, five threads can run simultaneously and can handle tasks from different users. The results of simulations show that the algorithm is adaptive and can make optimized offloading decisions on time, even in a time-varying Internet environment, with a significant increase in computational efficiency compared to traditional methods and other reinforcement learning methods.

关键词： Satellites   Task analysis   Servers   Delays   Low earth orbit satellites   Computer architecture   Computational modeling   Aerial computing   computation offloading   deep reinforcement learning   mobile edge computing   multi-agent system  

摘要： LEO satellite networks have become a necessary supplement to terrestrial networks aiming to provide worldwide, ubiquitous connectivity, especially in complicated areas (e.g., mountains, oceans, and disaster areas) where terrestrial network infrastructures are typically sparingly distributed or unavailable. However, the increasing computation-intensive Internet-of-Things (IoT) applications (e.g., real-time remote monitoring, intelligent transportation) require not only efficient and reliable communication but also massive computing capabilities. Constrained by the battery and computing resources, the computing tasks and data of applications have to be transmitted to remote cloud servers. This bandwidth limitation and high transmission delay in LEO networks will reduce the quality-of-service (QoS) of IoT applications. Recently, the combination of LEO networks and edge computing (i.e., Satellite Mobile Edge Computing, SMEC) offers significant opportunities to address these problems. The IoT devices can directly get the computing resources directly from satellites rather than remote servers, thus avoiding long-distance transmission. Considering the resource constraints on satellites, offloading policy plays a crucial role in whole system performance. In this paper, we design a hybrid offloading architecture, which applies a centralized training and distributed execution framework. Also, we propose a multi-agent actor-critic reinforcement learning algorithm, where a centralized "critic" is augmented with the global network state to ease the training procedure of distributed user equipments (UE) by evaluating the benefits of their decisions, while the UEs can adjust their policies according to the critic's evaluation and choose their own decisions relying on their observations.

关键词： Task analysis   Servers   Batteries   Optimization   Internet of Things   Schedules   Resource management   Mobile edge computing   wireless power transfer   energy-efficient   resource allocation   deep Q-network   convex optimization  

摘要： Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as "TS-DQN," for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

关键词： Mobile edge computing   Offloading decision   Response time   Energy consumption   Load balance   Multi-objective optimization immune algorithm  

摘要： In the mobile edge computing scenario, a large number of computing tasks are offloaded to the edge computing server, which may cause the tasks not to be processed on time. In this paper, the response time of the computing tasks, the energy consumption of the mobile terminal device and the load balance of the server were regarded as three optimization objectives, a multi-objective optimization model were set up, and an offloading decision scheme based on multi-objective optimization immune algorithm was proposed. A large number of comparative experiments are done to verify the effectiveness of proposed scheme. Experimental results show that the proposed scheme can make the whole server system achieve a better load balancing state while meeting the requirements of response time and energy consumption.