Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.
With the rapid growth in the number of IoT devices at the edge of the network, fast, flexible and secure edge computing has emerged, but the disadvantage of the insufficient computing power of edge servers is evident when dealing with massive computing tasks. To address this situation, firstly, a software-defined edge-computing architecture (SDEC) is proposed, merging the control layer of the software-defined architecture with the edge layer of edge computing, where multiple controllers share global information about the network state through an east–west message exchange, providing global state for the collaboration of edge servers. Secondly, a reinforcement-learning-based software-defined edge task allocation algorithm (RL-SDETA) is proposed in the software-defined IoT, which enables controllers to allocate computational tasks to the most appropriate edge servers for execution based on the global network information they have obtained. Simulation results show that the RL-SDETA algorithm can effectively reduce the finding cost of the optimal edge server and reduce the task completion time and its energy consumption compared to various task allocation methods such as random and uniform.
With the amount of data generated by Internet of Things (IoT) devices increase dramatically, the insufficient computing ability of terminal devices becomes obvious when processing massive computing tasks. The computing tasks need to be offloaded from resource-constrained devices to edge servers with stronger computing capability. It is a challenge for computing offloading to achieve global optimization with multiple objectives such as minimizing task completion times, optimizing energy consumption and maintaining load balancing as the network state and task demands dynamically change. This paper presents optimized edge computing offloading algorithm for software-defined IoT. First, to provide global state for making decisions, a software defined edge computing (SDEC) architecture is proposed. The edge layer is integrated into the control layer of software-defined IoT, and multiple controllers share the global network state information via east-west message exchange. Moreover, an edge computing offloading algorithm in software-defined IoT (ECO-SDIoT) based on deep reinforcement learning is proposed. It enables the controllers to offload the computing task to the most appropriate edge server according to the global states, task requirements, and reward. Finally, the performance metrics for edge computing offloading were evaluated in terms of unit task processing latency, load balancing of edge servers, task processing energy consumption, and task completion rate, respectively. Simulation results show that ECO-SDIoT can effectively reduce task completion time and energy consumption compared with other strategies.
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