关键词： Mobile edge computing   Pricing   PSO   Stackelberg game   Task offloading  

摘要： The task offloading technology in mobile edge computing can improve the benefits of each device. Moreover, designing reasonable prices for computing resources is critical to balancing the interests of end servers and devices. However, unbalanced information availability complicates this issue. To address this problem, the Stackelberg game is introduced to describe the relationship between the MEC server and end users (EUs). Then, the task offloading decision problem of EUs is simplified and proved by derivation. Last, uniform pricing and differentiated pricing algorithms are proposed. The differentiated pricing strategy is proposed by the PSO-based pricing optimization algorithm (DPOA), which can precisely constrain the offloading of each EU. Simulation results show the DPOA effectively improve profit of the server, and reduce latency of EUs.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) mainly offers strong computing capabilities and functions to finish the delay-sensitive task in time with the help of 5G wireless networks. Task scheduling is a technique for managing the increasing number of mobile edge users, decreasing task execution time, and improving the system's load-balancing capabilities. To achieve these goals, a distributed task scheduling system is developed in this research to satisfy multi-objectives such as cost, total execution time, overhead, and energy consumption for large-scale MEC tasks. First, a Hybrid Fuzzy Archimedes (HFA) algorithm is proposed to select the MEC node, which finishes the tasks with minimal cost and a higher security level. In the second step, the Hybrid LGBM and XGBoost architecture is formed to minimize the energy consumption and latency of each node for distributed task scheduling. The HFA algorithm modifies the search behavior of the Archimedes optimization algorithm using the fuzzy tendency factor and a normalized objective function. The HFA algorithm mainly selects the rule with an improved security value and lower cost for delay-sensitive applications. The main aim of the hybrid LGBM-XGBoost architecture is to minimize energy consumption and latency by taking the makespan and energy values. The efficiency of the proposed methodology is evaluated in terms of resource utilization, average completion time, completion rate, and Computation Workload Completion Rate. The proposed model offers a 20% improvement in average completion time and a 30% improvement in the energy consumption ratio. When 64 users are present in the system, the proposed model offers a CPU usage of 22% whereas MOCOSC, ADMM, and ANNIDS approaches offer CPU utilization of 62%, 78%, and 82%, respectively.

关键词： Mobile edge computing   Computation offloading   Deep reinforcement learning   Neural fictitious self-play   Proximal policy optimization   Quality of experience  

摘要： In Mobile Edge Computing (MEC), offloading tasks from mobile devices to edge servers may accelerate the processing speed and save the energy of the devices, hence improving device users' quality of experience. Recently, reinforcement learning (RL) is increasingly used for offload decision making. RL seeks long-term cumulative benefits and is proved useful for a sequence of decisions, thus is well suited for the work. Due to privacy and security concerns, mobile devices may be unwilling to expose their local information, leading to a fully decentralized MEC environment. Independent RL (IRL) emerges as a promising solution for this scenario. However, IRL solutions are faced with the non-stationarity issue, which arises when the components are changing their policies. In this paper, we proposing adopting the Neural Fictitious Self-Play (NFSP) architecture for offload decision making. NFSP explicitly tackles the non-stationarity issue with the built-in self-play mechanism, and uses a mixed strategy consisting of deep RL and the past average strategy, which is approximated by supervised deep learning. Furthermore, we use the Proximal Policy Optimization (PPO) algorithm as the RL component and exploit the Gated Recurrent Unit (GRU) to deal with the partial-observability issue in fully decentralized MEC. We conduct extensive simulation experiment, the result of which shows that our method outperforms the raw IRL approaches, validating the effectiveness of our proposed method.(c) 2022 Elsevier B.V. All rights reserved.

关键词： reinforcement learning   interning of things   mobile edge computing   computation offloading   linear regression  

摘要： Various edge collaboration schemes that rely on reinforcement learning (RL) have been proposed to improve the quality of experience (QoE). Deep RL (DRL) maximizes cumulative rewards through large-scale exploration and exploitation. However, the existing DRL schemes do not consider the temporal states using a fully connected layer. Moreover, they learn the offloading policy regardless of the importance of experience. They also do not learn enough because of their limited experiences in distributed environments. To solve these problems, we proposed a distributed DRL-based computation offloading scheme for improving the QoE in edge computing environments. The proposed scheme selects the offloading target by modeling the task service time and load balance. We implemented three methods to improve the learning performance. Firstly, the DRL scheme used the least absolute shrinkage and selection operator (LASSO) regression and attention layer to consider the temporal states. Secondly, we learned the optimal policy based on the importance of experience using the TD error and loss of the critic network. Finally, we adaptively shared the experience between agents, based on the strategy gradient, to solve the data sparsity problem. The simulation results showed that the proposed scheme achieved lower variation and higher rewards than the existing schemes.

关键词： blockchain   D2D   fifth-generation   mobile edge computing   optimization algorithm   the insurance industry  

摘要： Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two-stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step-by-step calculation method that is not feasible under real-time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.

关键词： Task analysis   Delays   Artificial intelligence   Computational modeling   Energy consumption   Servers   Inference algorithms   Mobile augmented reality   edge intelligence   mobile edge computing   resource allocation  

摘要： The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to reduce inference delays, energy consumption and to improve recognition accuracy. We give a simplified expression of the inference complexity model and accuracy model through derivation and experimentation. The problem is then solved iteratively by using alternating optimization. Specifically, by assuming that the offloading decision is given, the problem is decoupled into two sub-problems, i.e., the resource allocation problem for the devices set that completes the inference tasks locally, and that for the devices set that offloads tasks. For the problem of offloading decision optimization, we propose a Channel-Aware heuristic algorithm. To further reduce the complexity, we propose an alternating direction method of multipliers (ADMM) based distributed algorithm. The ADMM-based algorithm has a low computational complexity that grows linearly with the number of devices. Numerical experiments show the effectiveness of proposed algorithms. The trade-off relationship between delay, energy consumption, and accuracy is also analyzed.

关键词： Absolute performance bound   asymptotic performance bound   device-edge-cloud fusion   energy-constrained task scheduling   heuristic algorithm   mobile edge computing  

摘要： Mobile edge computing with device-edge-cloud fusion provides a new type of heterogeneous computing environment. We consider task scheduling with device-edge-cloud fusion (without energy concern) and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems. The main contributions of the paper are summarized as follows. We design three heuristic algorithms for task scheduling with device-edge-cloud fusion and prove an asymptotic performance bound. We design one heuristic algorithm for energy-constrained task scheduling with device-edge-cloud fusion, which solves the two subproblems of task scheduling and power allocation in an interleaved way. We derive lower bounds for the optimal solutions for both task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion, so that the performance of our heuristic algorithms can be compared with that of an optimal algorithm. We experimentally evaluate the performance of our heuristic algorithms and find that the performance of our heuristic algorithms are very close to that of optimal algorithms. To the best of our knowledge, this is the first paper which studies task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems and conducts analytical performance evaluation.

关键词： Service function chaining   Mobile edge computing   Actor-critic   Zero -forcing beamforming   Proximal center dual decomposition  

摘要： As two promising paradigms in emerging 6G wireless systems, service function chaining (SFC) and mobile edge computing (MEC) have attracted insensitive attentions from both industry and academia, and would bring more close-proximity services to 6G users with communication, computing and caching (3C) resources, yet also faced with challenges arising in time-varying channel conditions and resource dynamics. In this work, boosted by recent advents in artificial intelligence and reinforcement learning, we investigate the on-line SFC deployment in the edge of 6G wireless systems via the actor- critic learning framework. First, one long-run cost-efficient SFC deployment problem is investigated, and the coordinated multiple points (CoMP)-based zero-forcing beamforming is utilized to cancel the interference across SFCs. Then, by exploiting the Markov decision processes (MDP) property of long-run SFC deployment, one natural gradient-based actor-critic framework is proposed to characterize edge network dynamics, and meanwhile facilitates the training of neural networks to the global optimum. Next, to lower the size of action space, we follow the principle that a subproblem is embedded into each state-action pair's critic to solve the reward function, and then utilize both the lp (0 < p < 1) norm-based successive convex approximation (SCA) and proximal center-based dual decomposition to approach the global optimum and accelerate the convergence. Finally, numerical results are used to validate proposed actor-critic approach, showing that the communication resource management deserves special attentions in the SFC deployment in the edge of 6G wireless systems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Edge computing   Resource management   Task analysis   Optimization   Relays   Probability   Power system reliability   Computation offloading outage probability   min-max fairness   mobile edge computing   reconfigurable intelligent surface  

摘要： Reconfigurable intelligent surface (RIS) is able to enhance the wireless channels, and further improve the performance in terms of outage in the mobile edge computing (MEC) system. Taking the computation offloading outage probability (COOP) as the metric, we investigate the coverage performance of the RIS-assisted MEC system. Specifically, we first obtain the closed-form expression of the COOP. Then we derive an asymptotic COOP expression at the high transmit power region and minimize the COOP in the single-smart device (SD) scenario. Finally, aiming at ensuring fairness among SDs in the multi-SD scenario, we propose an alternating optimization-based COOP min-max fairness (AOCMMF) algorithm to obtain the suboptimal solution of the nonconvex min-max problem. The simulation results verify that our closed-form and asymptotic expressions depict the COOP correctly. The proposed AOCMMF scheme can reduce the maximum COOP by 42.23% compared with the equal edge computing resource scheme, and the min-max fairness among SDs is realized.

关键词： multi-objective optimization   mobile edge computing   unmanned aerial vehicle   differential evolution algorithm  

摘要： The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.