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Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of users, heterogeneous applications and intermittent traffic. In such environment, edge computing often suffers from unbalance resource allocation, which leads to task failure and affects system performance. To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and the failed task rate. Meanwhile, We adopt Deep-Q-Network(DQN) algorithms to solve the complexity and high dimension of workload scheduling problem. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine(VM) utilization, and failed tasks rate compared with other approaches. Our DRL-based approach can provide an efficient solution to the workload scheduling problem in edge computing.

With more complex user needs, the web service composition (WSC) has become a key research area in the current circumstance. The swarm intelligence algorithms are proved to solve this problem well. However, no researchers have applied the whale optimization algorithm (WOA) to the WSC problem. In this work, we propose a logarithmic energy whale optimization algorithm (LEWOA) based on aggregation potential energy and logarithmic convergence factor to solve this problem. Firstly, the improved algorithm uses a chaotic strategy to enhance the initial swarm diversity. After that, a logarithmic convergence factor is applied to obtain the nonlinear search step. Furthermore, aggregation potential energy as the spatial evaluation is employed in the swarm intelligence algorithms for the first time. Finally, the aggregation potential energy is used to dynamically adjust the nonlinear weight, which improves the search efficiency and prevents the algorithm from falling into local optimization. The experimental results of the benchmark functions show that the LEWOA has better optimization ability and convergence speed than other swarm intelligence algorithms. In the second experiment of the WSC optimization, the effectiveness and superiority of the LEWOA are verified.

Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of the end user, heterogeneous applications and intermittent traffific. In such environment, edge computing always encounters workload scheduling problem of how to effificiently schedule incoming tasks from mobile devices to edge servers or cloud servers, which is a hard and online problem. In this work, we focus on the workload scheduling problem with the goal of balancing the workload, reducing the service time and minimizing the failed task rate. We proposed a reinforcement learning-based approach, which can learn from the previous actions and achieve best scheduling in the absence of a mathematical model of the environment. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine utilization, and failed tasks rate compared with other approaches. Our reinforcement learning-based approach can provide an effificient solution to the workload scheduling problem in edge computing.