Abstract
Vehicular Fog Computing (VFC) is a promising technique to enable ultra low service latency by exploiting the computation and storage resources of both Roadside Units (RSUs) and Serving Vehicles (SVs) such as buses and trams with rich resources. To tackle with the mobility of vehicles, the services are usually migrated between RSUs and SVs, i.e., follow the vehicle, to maintain the benefits of VFC. However, making optimal service migration decisions in VFC is challenging due to the mobility of SVs and the interference between vehicles. In this paper, we investigate multi-vehicle service migration problem in VFC. We propose an efficient online algorithm, called FEE, to optimize the service migration for each vehicle in each time slot, where the latency in the current time slot, the expected latency in future time slots, and the interference among vehicles are minimized. The expected latency in future times slots is obtained by trajectory prediction based on hidden Markov model, and the interference is measured based on the server load. Finally, a series of simulations based on real-world mobility traces of Rome taxis are conducted to verify the superior performance of the proposed FEE algorithm as compared with the state-of-the-art solutions.
Highlights
With the development of autonomous driving, a variety of novel computation-intensive and delay-sensitive vehicular services, e.g., surrounding vehicle perception, high definition (HD) mapping, has emerged and posed great challenges on the conventional vehicular networks that supported by cellular networks and cloud computing [1], [2]
The expected latency in future times slots is obtained by trajectory prediction based on hidden Markov model, and the interference between vehicles is measured based on the server load
In this paper, we investigate the service migration problem in Vehicular Fog Computing (VFC) networks consists of fixed position base station (BS), moving Serving Vehicles (SVs) and vehicles
Summary
With the development of autonomous driving, a variety of novel computation-intensive and delay-sensitive vehicular services, e.g., surrounding vehicle perception, high definition (HD) mapping, has emerged and posed great challenges on the conventional vehicular networks that supported by cellular networks and cloud computing [1], [2]. The expected latency in future times slots is obtained by trajectory prediction based on hidden Markov model, and the interference between vehicles is measured based on the server load. This makes our algorithm scalable to multi-vehicle networks with low time complexity. We briefly survey existing literature in service migration from the perspective of VFC networks [22], [23], service latency and trajectory prediction [24]. Zhang et al [29] propose an online service migration method based on network efficiency optimization to maintain latency performance while optimizing energy efficiency These works neglect the interference among users, which leads to QoE deterioration.
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