关键词： Mobile edge computing   Virtual machine   I   O interference   Task scheduling   Mixed-integer nonlinear programming   Karush-Kuhn-Tucker  

摘要： Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   virtualized network function   VNF parallelization   VNF deployment   service function graph  

摘要： Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.

关键词： Mobile edge computing   Cost minimization   Markov decision process   Deep reinforcement learning   Power allocation  

摘要： Mobile edge computing (MEC) can dispatch its powerful servers close by to assist with the computation workloads that intelligent wireless terminals have offloaded. The MEC server's physical location is closer to the intelligent wireless terminals, which can satisfy the low latency and high reliability demands. In this paper, we formulate an MEC framework with multiple vehicles and service devices that considers the priority and randomness of arriving workloads from roadside units (RSUs), cameras, laser radars (Lidar) and the time-varying channel state between the service device and MEC server (MEC-S). To minimize the long-term weighted average cost of the proposed MEC system, we transit this issue (cost minimization problem) into the Markov decision process (MDP). Furthermore, considering the difficulty realizing the state transition probability matrix, the dimensional complexity of the state space, and the continuity of the action space, we propose a deterministic policy gradient (MADDPG)-based bandwidth partition and power allocation optimization policy. The proposed MADDPG-based policy is a model-free deep reinforcement learning (DRL) method, which can effectively deal with continuous action space and further guide multi-agent to execute decision-making. The comprehensive results verify that the proposed MADDPG-based optimization scheme has fine convergence and performance that is better than that of the other four baseline algorithms.

关键词： Mobile edge computing   Edge server selection   Resource allocation   Deep Q-learning  

摘要： This paper discusses the challenges of mobile edge computing with low latency and energy consumption caused by the explosive growth of communication traffic and data generated by mobile devices. To address the issue of selecting edge servers, a multi-user-oriented edge server selection strategy is proposed. The strategy minimizes the weighted sum of network latency and total energy consumption and develops an improved SPEA2-based algorithm to choose the most suitable edge server. The resource allocation problem is also considered, and a resource allocation strategy is proposed that maximizes the energy efficiency ratio. A DQN-based resource allocation strategy is devised to find the best resource allocation strategy. Experimental results demonstrate that the proposed server selection strategy reduces system overhead in terms of energy consumption and latency while improving resource utilization. The resource allocation strategy improves the computational efficiency of edge servers while reducing latency and total energy consumption.

关键词： Mobile edge computing   task offloading   high speed railway   fluctuation of transmission and processing   potential game   stochastic network calculus  

摘要： The advanced 5G and mobile edge computing promote the development of the intelligent high-speed railway and enable computing-intensive and latency-sensitive tasks. Mobile edge computing can effectively release the pressure of tasks on on-board computing resources by migrating communication, computing, and storage to edge servers. However, the fluctuation of data transmission and task processing duration exists in practical projects. The current work assumes that the traditional communication model is ideal and that the latency can be derived as a fixed value, which is the most conservative bound. Obviously, there will be some system performance loss because the transmission latency is usually much shorter than this conservative bound. Therefore, an offloading strategy is proposed to maximize the task completion rate, considering the fluctuation of transmission latency and processing time in this paper. First, the stochastic network calculus is adopted to evaluate the fluctuation and probability of the transmission latency. Additionally, the task processing duration is regarded as an exponential distribution that is affected by the computing resource. Then, the task offloading model is generated based on the potential game to maximize the task completion rate. Moreover, the Nash Equilibrium and best response are derived. A reinforcement learning algorithm combined with a game is proposed to reach the Nash Equilibrium and obtain the task offloading strategy. Finally, extensive theoretical analysis and simulations are illustrated to prove the effectiveness of the model.

关键词： Autonomous aerial vehicles   Task analysis   Trajectory   Energy consumption   Wireless communication   Uncertainty   Relays   Mobile edge computing   unmanned aerial vehicle   channel uncertainty   information security  

摘要： This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users' quality of service and information security requirements and the UAV's trajectory's causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

关键词： Deep reinforcement learning   industrial Internet of Things   mobile edge computing   online offloading   time-energy tradeoff  

摘要： Mobile edge computing (MEC) has recently emerged as a promising technology to boost the integration ability of sensing, transmission and computation in industrial Internet of Things (IIoT). This paper investigates an MEC-enabled IIoT system, where multiple industrial devices may offload computation-intensive tasks to an edge server through wireless communication. We focus on the online offloading problem to optimize the tradeoff of the task accomplishing time and energy consumption. Time-varying wireless channels, random targeted task data sizes and dynamically changing residual energy as well as adaptively adjusted tradeoff weights make this problem highly challenging. Conventional optimization methods may lead to inefficient or even infeasible solutions. To efficiently tackle this problem, we leverage the deep reinforcement learning (DRL) technology to propose a time-energy tradeoff online offloading algorithm called TETO. In TETO, the online offloading decision policies are empirically learned via a well-designed DRL framework. TETO algorithm incorporates a stochastic strategy, the crossover and mutation technology and a novel feasible suboptimal offloading method to expand the offloading action search space with the provable feasibility guarantee. Extensive experimental results based on a real-world dataset show that, our TETO algorithm performs better than existing baseline algorithms, and obtains near-optimal performance with low CPU execution latency.

关键词： mobile edge computing   task offloading   optimal association   vertical scaling   horizontal scaling  

摘要： Compared to cloud computing, mobile edge computing (MEC) is a promising solution for delay-sensitive applications due to its proximity to end users. Because of its ability to offload resource-intensive tasks to nearby edge servers, MEC allows a diverse range of compute- and storage-intensive applications to operate on resource-constrained devices. The optimal utilization of MEC can lead to enhanced responsiveness and quality of service, but it requires careful design from the perspective of user-base station association, virtualized resource provisioning, and task distribution. Also, considering the limited exploration of the federation concept in the existing literature, its impacts on the allocation and management of resources still remain not widely recognized. In this paper, we study the network and MEC resource scheduling problem, where some edge servers are federated, limiting resource expansion within the same federations. The integration of network and MEC is crucial, emphasizing the necessity of a joint approach. In this work, we present NAFEOS, a proposed solution formulated as a two-stage algorithm that can effectively integrate association optimization with vertical and horizontal scaling. The Stage-1 problem optimizes the user-base station association and federation assignment so that the edge servers can be utilized in a balanced manner. The following Stage-2 dynamically schedules both vertical and horizontal scaling so that the fluctuating task-offloading demands from users are fulfilled. The extensive evaluations and comparison results show that the proposed approach can effectively achieve optimal resource utilization.

关键词： Mobile edge computing   reinforcement learning   satellite-terrestrial network   task offloading   velocity control  

摘要： Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

关键词： Sensors   Internet of Things   Servers   Task analysis   Optimization   Monitoring   Process control   Age of information   mobile edge computing   computation offloading   status update  

摘要： IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.