Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (1901.05205v1)

Abstract: The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.

• The paper introduces ALTO, a novel algorithm based on multi-armed bandit theory that minimizes task offloading delay in dynamic vehicular networks.
• It employs input-awareness and occurrence-awareness to balance exploration and exploitation according to task size and vehicle availability.
• Simulations demonstrate that ALTO cuts delay by up to 30% compared to traditional methods, validating its practical efficiency in realistic highway scenarios.

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

The research paper titled "Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems" explores an innovative approach to manage task offloading in Vehicular Edge Computing (VEC) environments. The paper addresses the challenges posed by the dynamic and uncertain VEC systems, such as rapidly changing network topologies, fluctuating wireless channel states, and varying computing workloads, which complicate optimal decision-making for task offloading. In this context, the authors propose an Adaptive Learning-Based Task Offloading (ALTO) algorithm built on multi-armed bandit (MAB) theory to efficiently manage these uncertainties.

Overview

The paper first contextualizes the need for efficient task offloading in vehicular networks, as these are essential for supporting increasingly prevalent automotive applications such as autonomous driving and intelligent infotainment systems. The VEC system can capitalize on the computational capabilities of vehicles themselves, which may reach substantial levels in terms of DMIPS, considerably surpassing conventional computing devices.

In response to the unpredictable nature of VEC environments, the ALTO algorithm offers a distributed, learning-based solution. By eschewing frequent state information exchange, ALTO enables each vehicle to learn the offloading performance of nearby vehicles autonomously, thereby reducing signaling overhead.

Technical Contributions

1. Algorithm Design: The proposed ALTO algorithm operates under the MAB framework, focusing on minimizing average offloading delay. It is enhanced with input-awareness and occurrence-awareness to adaptively respond to dynamic VEC conditions. Input-awareness allows the algorithm to adjust its exploration-exploitation balance based on task size, while occurrence-awareness accounts for the sporadic presence of service vehicles.
2. Guaranteed Sublinear Regret: Theoretical analysis demonstrates that ALTO achieves a sublinear learning regret, indicating consistent improvement of the decision process over time. This ensures the algorithm effectively learns to optimize task offloading decisions even in environments where network conditions and vehicle availability vary significantly.
3. Empirical Validation: Through extensive simulations, both synthetic and based on realistic highway scenarios, ALTO shows a significant reduction in average delay—up to 30% compared to traditional upper confidence bound strategies—validating its effectiveness and efficiency in practical settings.

Implications and Potential Impact

The introduction of adaptive learning mechanisms into VEC systems is a significant advancement, as it promises enhanced reliability and minimized delay without burdensome computational and communication overhead. This work reminds researchers of the potential for MAB and other online learning frameworks to address real-time decision-making challenges in dynamic environments like VEC.

In terms of practical implications, the results suggest that automotive industries could deploy this framework to improve the efficiency of VEC systems, optimizing the user experience for real-time applications. Theoretically, it opens pathways for further research into adaptive learning strategies in networks where uncertainty and variability are inherent.

Future Directions

Future research could expand on this framework by considering more complex vehicular environments, such as urban settings, where adversarial bandit approaches might be more appropriate. Additionally, further exploration of resource allocation strategies in conjunction with task offloading could yield even more robust solutions for comprehensive VEC management. Integration with advanced communication protocols and the potential for scalability also warrant investigation, paving the way for adoption in smarter, more autonomous vehicular networks.