IRATS: A DRL-based intelligent priority and deadline-aware online resource allocation and task scheduling algorithm in a vehicular fog network

Cloud computing platforms support the Internet of Vehicles, but the main bottlenecks are high latency and massive data transmission in cloud-based processing. Vehicular fog computing has emerged as a promising paradigm to accommodate the increasing computational needs of vehicles. It provides low la...

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Vydáno v:	Ad hoc networks Ročník 141; s. 103090
Hlavní autoři:	Jamil, Bushra, Ijaz, Humaira, Shojafar, Mohammad, Munir, Kashif
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 15.03.2023
Témata:	Deep reinforcement learning Proximal policy optimization Resource allocation Task scheduling Vehicular fog network Task scheduling Deep reinforcement learning Proximal policy optimization Vehicular fog network Resource allocation
ISSN:	1570-8705, 1570-8713
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Cloud computing platforms support the Internet of Vehicles, but the main bottlenecks are high latency and massive data transmission in cloud-based processing. Vehicular fog computing has emerged as a promising paradigm to accommodate the increasing computational needs of vehicles. It provides low latency network services that are most important for latency-sensitive tasks. The dynamic nature of VFC, having vehicles with heterogeneous computing resources, vehicle mobility, and diverse tasks with different priorities are the main challenges in vehicular fog networks. In VFC, vehicles can share their idle compute resources with other task-generating vehicles. So, scheduling the tasks on the idle resources of resource-limited vehicles is very important. Existing solutions use a heuristic approach to solve this issue but lack generalizability and adaptability. In this paper, we describe a PPO-based intelligent, priority and deadline-aware online and distributed resource allocation and task scheduling algorithm, called IRATS, in vehicular fog networks. IRATS formulates the resource allocation problem as a Markov decision process to minimize the waiting time and delay of tasks. For vehicles sharing their idle resources, we design a task scheduler for the orderly execution of received tasks according to their priorities using multi-level queues. We conducted extensive simulations using SUMO, OMNeT++, Veins, and veins-gym to validate the effectiveness of the presented algorithm. The simulation results confirm that the proposed algorithm improves the percentage of in-time completed tasks and decreases the packet loss, waiting time, and end-to-end delay as compared to random, A2C, and DQN algorithms considering the task priority and link duration of vehicles.
ISSN:	1570-8705 1570-8713
DOI:	10.1016/j.adhoc.2023.103090