To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging.Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic X. Chen is with the ). M. Bennis is with the Centre for Wireless Communications, University of Oulu, Finland (email: bennis@ee.oulu.fi). 2 computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies. Index Terms Network slicing, radio access networks, network virtualization, mobile-edge computing, Markov decision process, deep reinforcement learning, Q-function decomposition. I. INTRODUCTION With the proliferation of smart mobile devices, a multitude of mobile applications are emerging and gaining popularity, such as location-based virtual/augmented reality and online gaming [1]. However, mobile devices are in general resource-constrained, for example, the battery capacity and the local CPU computation power are limited. When executed at the mobile devices, the performance and Quality-of-Experience (QoE) of computation-intensive applications are significantly affected by the devices' limited computation capabilities. The tension between computation-intensive applications and resource-constrained mobile devices creates a bottleneck for having a satisfactory Quality-of-Service (QoS) and QoE, and is hence driving a revolution in computing infrastructure [2]. In contrast to cloud computing, mobile-edge computing (MEC) is envisioned as a promising paradigm, which provides computing capabilities within the radio access networks (RANs) in close proximity to mobile users (MUs) [3]. By offloading computation ta...
With the emerging vehicular applications such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in Vehicular Fog Computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.
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