Computation Offloading Scheduling And Resource Management Strategy In Mobile Edge Computing

By distributing computation and storage resources to the network edge in the vicinity of users and data sources,mobile edge computing(MEC)supports mobile applications to complete their computation offloading process within the radio access network.Such a novel computing architecture significantly reduces the end-to-end network latency and effectively releases the burden of the core network and data center.Offloading decisionmaking,including user-side decision-making(e.g.,whether to offload,how to offload,and when to offload)and operator-side decision-making(e.g.,offloading request admission and resource allocation),is the key to efficiently utilize MEC.Due to the complexity of MEC environments,the decision-making process is affected by many factors.How to design the optimal offloading decision strategy to fully exploit the potential of MEC in terms of latency and energy consumption is a very challenging scientific problem.Task scheduling and resource management are two crucial factors in MEC offloading decision process.On one hand,the MEC environment is essentially a distributed heterogeneous parallel computing environment.Only with reasonable task scheduling,the potential of such a computing environment can be fully exploited.When considering the dynamics of the wireless network,it is necessary to properly determine the timing of task scheduling.On the other hand,the limited resources deployed at the network edge need to be allocated appropriately to maximize their utility.It is also necessary to perform admission decisions among offloading requests to avoid excessive resource contention.To this end,the dissertation focuses on three offloading decision scenarios(i.e.,the offloading scheduling of tasks with graph dependency in a static environment from the perspective of users,the offloading scheduling of tasks in a complex task queue in a dynamic environment from the perspective of users,and the offloading admission and resource allocation considering user mobility from the perspective of operators)to explore the optimal strategy for offloading scheduling and resource management.First,the dissertation studies the decision-making problem of offloading scheduling on tasks with directed acyclic graph(DAG)dependency in a static environment from the perspective of users,while fully considering the limited computation and communication resources at the network edge.A general DAG-oriented offloading scheduling algorithm based on deep reinforcement learning(DRL)is proposed,which can achieve two different offloading scheduling goals: minimizing execution latency and maximizing user utility.Specifically,the offloading scheduling decision process on tasks with DAG dependency is modeled as a Markov decision process(MDP).A recurrent neural network-based sequence to sequence parameter-shared deep neural network architecture,as well as the corresponding DAG embedding method,are proposed to approximate the MDP offloading scheduling policy,which is then trained based on state-of-the-art proximal policy optimization(PPO).The effectiveness and reliability of the proposed algorithm are verified through comparison with six baseline algorithms in different environments and for different offloading scheduling goals.Furthermore,also from the perspective of users,the offloading scheduling decisionmaking problem in a highly dynamic vehicular MEC environment is studied,in which all the dynamic factors,such as task arrival,task attribute,wireless channel,and user mobility,are taken into account.This stochastic optimization problem is very difficult for conventional solutions because of the sophisticated environment dynamics and vast state space.A DRL-based dynamic offloading scheduling algorithm is designed,which jointly solves “where” to schedule and “when” to schedule each task in the complex task queue,to achieve the optimal long-term tradeoff between task latency and energy consumption in such a complicated environment.A series of methods are adopted to improve the training efficiency and convergence performance of the algorithm,including utilizing PPO to ensure the efficiency and stability of the training process,embedding a convolutional neural network into the policy network to extract the key features of the complex task queue,and adjusting the state and reward of DRL to avoid inefficient exploration in the training process.Extensive simulation experiments demonstrate that the performance of the proposed algorithm is much higher than that of traditional baseline algorithms in different environments and user preferences.Finally,from the perspective of operators,the admission decision and resource allocation strategy among multiple moving users in a vehicular MEC environment is investigated,in which the constraints of computation and communication resource limitation,task deadline requirements,and the mobility of users are taken into account,in order to maximize the overall system utility.The optimization problem is formulated as a mixed-integer non-linear programming(MINLP),and a heuristic multi-user mobilityaware offloading decision algorithm with polynomial time complexity is proposed.The original global optimization problem is converted into a finite number of local optimization problems through parameter control,and each local optimization problem is then decomposed into a convex subproblem and a non-linear integer programming(NLIP)subproblem.The convex subproblem is solved with a numerical method to obtain the optimal resource allocation,and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision.Finally,the solution to the global optimization problem is obtained by solving all the local optimization problems.Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate the excellent performance of the proposed algorithm.