Optimizing Computation Offloading and Task Scheduling in Multi-Server Multi-Access Edge Vehicular Networks

Core Concepts

This paper presents an efficient offloading scheme for multi-server multi-access edge vehicular networks by jointly considering the mobility and task priority of terminal devices. A double deep Q-network (DDQN)-based reward evaluation algorithm is proposed to handle a large number of offloading requests and fully utilize the server resources.

Abstract

The paper investigates a multi-user offloading problem in a multi-server mobile edge computing system for vehicular networks. The problem is divided into two stages: the offloading decision making stage and the request scheduling stage.
In the offloading decision making stage:

The mobility of terminal devices is considered to prevent them from going out of the service area during offloading.
A server evaluation mechanism based on mobility and server load is introduced to select the optimal offloading server.
In the request scheduling stage:

A DDQN-based reward evaluation algorithm is designed to fully utilize the server resources and prioritize important tasks when scheduling offload requests.
Numerical simulations show that the proposed scheme outperforms traditional mathematical computation methods and the DQN algorithm in terms of the number of important tasks accomplished per unit of time.

Stats

The size of the task Jm(t) is denoted as z(Jm(t)) in bits.
The CPU cycle frequency of terminal device Um is fm.
The maximum number of data bits that can be processed simultaneously by each CPU clock cycle of Um is ϑm.
The power consumption of Um in computing state and idle state are P comp
m and P idle
m , respectively.
The transmission power of Um is P tran
m .
The bandwidth of the offloading link is W.
The channel gain between the terminal device and the MEC server is |hm,s|2.
The noise power is σ2.
The CPU frequency of MEC server En is Fn.
The maximum number of data bits that can be processed simultaneously by MEC server En is ϑn.

Quotes

"To prevent the terminal from going out of service area during offloading, we consider the mobility parameter of the terminal according to the human behaviour model when making the offloading decision, and then introduce a server evaluation mechanism based on both the mobility parameter and the server load to select the optimal offloading server."
"In order to fully utilise the server resources, we design a double deep Q-network (DDQN)-based reward evaluation algorithm that considers the priority of tasks when scheduling offload requests."

Key Insights Distilled From

Computation Offloading for Multi-server Multi-access Edge Vehicular Networks

by Siyu Wang,Bo... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07215.pdf

Deeper Inquiries

How can the proposed offloading scheme be extended to handle more complex mobility patterns of terminal devices, such as random or unpredictable movements?

To handle more complex mobility patterns of terminal devices, such as random or unpredictable movements, the proposed offloading scheme can be extended by incorporating predictive algorithms based on historical data or real-time tracking. By utilizing machine learning models, such as recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, the system can predict the future positions of terminal devices more accurately. This predictive capability can enable proactive offloading decisions, considering the potential movement trajectories of the devices. Additionally, integrating advanced location tracking technologies like GPS and inertial sensors can provide real-time updates on terminal device positions, allowing for dynamic adjustment of offloading strategies based on the current mobility patterns.

What are the potential trade-offs between task priority, delay, and energy consumption that could be explored in the offloading decision-making process?

In the offloading decision-making process, there are several potential trade-offs between task priority, delay, and energy consumption that could be explored to optimize system performance. One trade-off involves balancing task priority with delay, where high-priority tasks may require immediate processing but could lead to increased delay if not offloaded efficiently. By assigning different levels of priority to tasks based on their criticality, the system can prioritize offloading decisions accordingly, potentially sacrificing lower-priority tasks to reduce overall delay.
Another trade-off exists between delay and energy consumption. Offloading tasks to remote servers can reduce local processing delay but may increase energy consumption due to data transmission and server processing. By dynamically adjusting the offloading strategy based on the energy efficiency of different servers and the urgency of tasks, the system can optimize the trade-off between delay and energy consumption. Additionally, considering the energy efficiency of offloading options and the impact on battery life can help in making informed decisions that balance performance and resource utilization effectively.

How can the DDQN-based scheduling algorithm be further improved to adapt to dynamic changes in the network environment, such as fluctuations in server load or channel conditions?

To enhance the adaptability of the DDQN-based scheduling algorithm to dynamic changes in the network environment, such as fluctuations in server load or channel conditions, several improvements can be implemented:

Dynamic Learning Rate Adjustment: Implementing a dynamic learning rate adjustment mechanism can help the algorithm adapt to changing network conditions. By monitoring performance metrics like task completion time and adjusting the learning rate based on the rate of convergence or divergence, the algorithm can optimize its learning process in real-time.

Incorporating Feedback Mechanisms: Introducing feedback mechanisms that provide information on server load, channel conditions, and task priorities can enable the algorithm to make more informed decisions. By continuously updating the state space with real-time feedback, the algorithm can adjust its scheduling strategies based on the current network environment.

Multi-Agent Reinforcement Learning: Employing a multi-agent reinforcement learning approach can enhance the algorithm's ability to handle complex interactions in dynamic environments. By allowing multiple agents to collaborate or compete in decision-making processes, the algorithm can better adapt to changing server loads and channel conditions while optimizing task scheduling.

Adaptive Exploration-Exploitation Strategy: Implementing an adaptive exploration-exploitation strategy can help the algorithm balance the exploration of new offloading strategies with the exploitation of known optimal solutions. By dynamically adjusting the exploration rate based on the stability of the network environment, the algorithm can effectively navigate fluctuations in server load and channel conditions while maximizing performance.