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Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed. [ABSTRACT FROM AUTHOR]

In the multi-server multi-access edge computing (MEC) system, the computing capacity could be insufficient and the computing resource could be over-utilized when an excessive number of computing tasks are offloaded for execution. To alleviate the excessive burden in the multi-server MEC system and tap the underutilized resources of wired edge devices, which are called edge computers in this paper, we investigate the computation offloading and shunting scheme in the wireless wireline internetwork. We formulate an optimization problem to minimize the average total time delay and use the deep reinforcement learning method to obtain the optimal shunting policy. By sensing the state of the wireless wireline internetwork, the proposed scheme can adaptively adjust the shunting ratio and utilize the resources of MEC servers and edge computers intelligently and jointly. Finally, we evaluate the computational complexity and validate the convergence property of this scheme. The extensive simulation results demonstrate that this scheme can improve the resource productivity and reduce the average total time delay. [ABSTRACT FROM AUTHOR]

The smart vehicles construct Internet of Vehicle (IoV), which can execute various intelligent services. Although the computation capability of a vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for intelligent vehicular services. When offloading the complex service to the vehicular edge computing node, the decision for its destination should be considered according to numerous factors. This paper mostly formulate the offloading decision as a resource scheduling problem with single or multiple objective function and constraints, where some customized heuristics algorithms are explored. However, offloading multiple data dependence tasks in a complex service is a difficult decision, as an optimal solution must understand the resource requirement, the access network, the user mobility, and importantly the data dependence. Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for IoV, which provides the optimal policy directly from the environment. We formulate the offloading decision for the multiple tasks as a long-term planning problem, and explore the recent deep reinforcement learning to obtain the optimal solution. It can scruple the future data dependence of the following tasks when making decision for a current task from the learned offloading knowledge. Moreover, the framework supports the pre-training at the powerful edge computing node and continually online learning when the vehicular service is executed, so that it can adapt the environment changes and can learn policy that are sensible in foresight. The simulation results show that KD service offloading decision converges quickly, adapts to different conditions, and outperforms a greedy offloading decision algorithm. [ABSTRACT FROM AUTHOR]