CUDA-accelerated task scheduling in vehicular clouds with opportunistically available V2I

Maalej, Yassine; Abderrahim, Ahmed; Guizani, Mohsen; Hamdaoui, Bechir

doi:10.1109/icc.2017.7997148

Cited by 3 publications

(3 citation statements)

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“…The Markov Decision Process (MDP) based vehicular task scheduling over VCs based on the opportunistically available V2I communications between VCs is presented in [121]. The MDP-based task scheduling in VCC is developed to optimize the task placement of various vehicular tasks over many VCs in a way that increases the maximum expected longterm cumulative reward of the VCC and not for minimizing the cost of VM migration from one VC to another.…”

Section: Software Defined Wireless Networking Fast Greedy Heuristic A...mentioning

confidence: 99%

“…As opposed to [120] where the placement of tasks is made based on minimizing VM migration over demand changes, a Fast greedy heuristic algorithm is presented in [121] in order to find sub-optimal solutions for virtual machine allocation and vehicular task placement based on tasks' requirements and VCs' availability, in terms of free VMs. The greedy scheduling algorithm does not reconsider its placements after a scheduling decision of tasks is made.…”

Section: Software Defined Wireless Networking Fast Greedy Heuristic A...mentioning

confidence: 99%

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Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

Maalej¹,

Balti²

2022

Preprint

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For decades, researchers on Vehicular Ad-hoc Networks (VANETs) and autonomous vehicles presented various solutions for vehicular safety and autonomy, respectively. Yet, the developed work in these two areas has been mostly conducted in their own separate worlds, and barely affect one-another despite the obvious relationships. In the coming years, the Internet of Vehicles (IoV), encompassing sensing, communications, connectivity, processing, networking, and computation is expected to bridge many technologies to offer value-added information for the navigation of self-driving vehicles, to reduce vehicle on board computation, and to deliver desired functionalities. Potentials for bridging the gap between these two worlds and creating synergies of these two technologies have recently started to attract significant attention of many companies and government agencies. In this article, we first present a comprehensive survey and an overview of the emerging key challenges related to the two worlds of Vehicular Clouds (VCs) including communications, networking, traffic modelling, medium access, VC Computing (VCC), VC collation strategies, security issues, and autonomous driving (AD) including 3D environment learning approaches and AD enabling deep-learning, computer vision and Artificial Intelligence (AI) techniques. We then discuss the recent related work and potential trends on merging these two worlds in order to enrich vehicle cognition of its surroundings, and enable safer and more informed and coordinated AD systems. Compared to other survey papers, this work offers more detailed summaries of the most relevant VCs and ADs systems in the literature, along with some key challenges and insights on how different technologies fit together to deliver safety, autonomy and infotainment services.

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Section: Software Defined Wireless Networking Fast Greedy Heuristic A...mentioning

confidence: 99%

Section: Software Defined Wireless Networking Fast Greedy Heuristic A...mentioning

confidence: 99%

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

Maalej¹,

Balti²

2022

Preprint

Self Cite

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“…In our solution, we focus on the acceleration and the total time to complete the kernel and not the duration it takes a thread to complete. We use NVIDIA's Quadro K1100M GPU, as shown in Table . II to leverage the parallel SIMD capabilities of CUDA SMs model by running the value iteration algorithm to finding the optimal solution, as initiated in [39]. Our BSDI implementation in Algorithm 4 uses parallel set of state space that do not have any relationship to apply the value iteration algorithm in parallel and evaluate the performance of all states after scheduling.…”

Section: Algorithm 3 Algorithm For Value Iterationmentioning

confidence: 99%

CUDA-Accelerated Application Scheduling in Vehicular Clouds Under Advanced Multichannel Operations in WAVE

Maalej,

Balti

2020

Preprint

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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-oftasks (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.Index Terms-vehicular cloud (VC), CUDA, dedicated short range communication (DSRC), wireless access vehicular environments (WAVE), vehicle to infrastructure (V2I), vehicle to vehicle (V2V), Markov decision process (MDP), IEEE 1609.4, network simulator-3 (NS3), service channel (SCH), Control channel (CCH).

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GPU-based Parallel Computing for VANETs: Current State and Future Prospects

Borah,

Paranjothi

2023

2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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CUDA-accelerated task scheduling in vehicular clouds with opportunistically available V2I

Cited by 3 publications

References 13 publications

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives

CUDA-Accelerated Application Scheduling in Vehicular Clouds Under Advanced Multichannel Operations in WAVE

GPU-based Parallel Computing for VANETs: Current State and Future Prospects

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