Computing Resource Allocation Strategy Based on Cloud-Edge Cluster Collaboration in Internet of Vehicles

Computing Resource Allocation Strategy Based on Cloud-Edge Cluster Collaboration in Internet of Vehicles

Blog Article

Edge computing plays a crucial role in the field of the Internet of Vehicles (IoV), meeting the resource and latency requirements of time-sensitive vehicle applications.However, the emergence of numerous compute-intensive and latency-sensitive applications, such as augmented reality and autonomous driving, has led to a situation where traditional edge computing architectures cannot meet the increasing application demands of the IoV.This paper extends the paradigm of vehicular edge computing to a collaborative cloud-edge cluster resource provisioning framework.Integrating compute resources from multiple Edge Service Providers (ESP) and nitrile gloves in a bucket the cloud enables horizontal and vertical collaborative computation offloading among service nodes.To facilitate resource sharing among different ESPs, we introduce a dynamic pricing model and utilize software-defined networking (SDN) to tackle this scenario’s complex resource management challenges.

Furthermore, with the optimization objectives of 3 piece horse wall art minimizing task computation latency and maximizing the profits of ESPs, we establish a mathematical model.Before resource allocation, we employ a clustering algorithm to determine initial offloading decisions, reducing the dimensionality of the action space.Subsequently, we employ the Double Deep Q-Network (DDQN) algorithm to achieve a rational allocation of compute resources.Simulation results demonstrate that compared to the Deep Q-Network (DQN) algorithm and greedy strategy, the proposed approach reduces latency by 18.18% and 34.

85%, respectively, while increasing the profits of edge service providers by 16.25% and 33.33%, respectively.