Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication

With the rapid development of the Internet of vehicles,the emerging new types of in-vehicle tasks put forward higher requirements for communication and computing capabilities.The development of satellite communication technology and the large-scale deployment of 5G millimeter-wave base stations provide safer and more reliable services for highway vehicle users.At the same time,mobile edge computing technology deploys mobile edge computing(MEC) servers with computing and storage capabi-lities around user terminals to provide computing services for on-board tasks while reducing transmission delays.Aiming at the problem of offloading decision-making and communication resource allocation of vehicle tasks in highway scenarios,the joint optimization problem of computing and communication resources is modeled as a 0-1 mixed integer linear programming problem.Firstly,the original optimization problem is decoupled into the resource block allocation sub-problem and the offloading decision sub-problem.Secondly,the sub-problems are solved by using the water injection algorithm and the particle swarm algorithm.Finally,the sub-problems are iteratively solved based on the heuristic algorithm to obtain the optimal resource block allocation scheme and offload decision vector.Simulation results show that the algorithm minimizes the average system delay while meeting the requirements of all on-board missions

SCMA-enabled multi-cell edge computing networks : design and optimization

Multi-access edge computing (MEC) is regarded as a promising approach for providing resource-constrained mobile devices with computing resources through task offloading. Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) scheme that can meet the demands of multi-cell MEC networks for high data transmission rates and massive connections. In this paper, we propose an optimization framework for SCMA-enabled multi-cell MEC networks. The joint resource allocation and computation offloading problem is formulated to minimize the system cost, which is defined as the weighted energy cost and latency. Due to the nonconvexity of the proposed optimization problem induced by the coupled optimization variables, we first propose an algorithm based on the block coordinate descent (BCD) method to iteratively optimize the transmit power and edge computing resources allocation by deriving closed-form solutions, and further develop an improved low-complexity simulated annealing (SA) algorithm to solve the computation offloading and multi-cell SCMA codebook allocation problem. To solve the problem of partial state observation and timely decision-making in long-term optimization environment, we put forward a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and distributed execution. Furthermore, we extend the framework to the partial offloading case and propose an algorithm based on alternating convex search for solving the task offloading ratio. Numerical results show that the proposed multi-cell SCMA-MEC scheme achieves lower energy consumption and system latency in comparison to the orthogonal frequency division multiple access (OFDMA) and power-domain (PD) NOMA techniques

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications