CUDA-Accelerated Application Scheduling in Vehicular Clouds Under Advanced Multichannel Operations in WAVE
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This paper presents a novel Advanced Activity-Aware (AAA) scheme to optimize and improve Multi-Channel Operations based on the IEEE 1609.4 standard in wireless access vehicular environments (WAVE). The proposed scheme relies on the awareness of the vehicular safety load to dynamically find an optimal setup for switching between service channel intervals (SCHI) and control channel intervals (CCHI). SCHI are chosen for non-critical applications (e.g. infotainment), while CCHI are utilized for critical applications (e.g. safety-related). We maximize the channel utilization without sacrificing other application requirements such as latency and bandwidth. Our scheme is implemented and evaluated network simulator-3 (NS3). We guarantee the default Synchronization Interval (SI), like implemented by the standard in vehicular ad hoc networks (VANETs), when tested on real-time simulations of vehicular cloud (VC) load and VANET setups using NS3. We also evaluate a Markov Decision Process (MDP) based scheme and a fast greedy heuristic to optimize the problem of vehicular task placement with both IEEE 1609.4 and an opportunistic V2I version of the proposed AAA scheme. Our solution offers the reward of the VC by taking into account the overall utilization of the distributed virtualized VCs resources and vehicular bag-of-tasks (BOTs) placements both sequentially and in parallel. We present the simulation metrics proving that our proposed solution significantly improve the throughput and decreases the average delay of uploaded packets used for non-safety applications, while maintaining reliable communication (via CCHI) for safety-related applications similar to the IEEE 1609.4 standard.
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