Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach
CoRR(2024)
摘要
The emergence of 5G networks has enabled the deployment of a two-tier edge
and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and
Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things
(IoT) devices, reducing propagation latency compared to cloud-based solutions
and ensuring satisfactory quality of service (QoS). However, during
high-traffic events like concerts or athletic contests, MEC sites may face
congestion and become overloaded. Utilizing offloading techniques, we can
transfer computationally intensive tasks from resource-constrained devices to
those with sufficient capacity, for accelerating tasks and extending device
battery life. In this research, we consider offloading within a two-tier MEC
and VF architecture, involving offloading from MEC to MEC and from MEC to VF.
The primary objective is to minimize the average system cost, considering both
latency and energy consumption. To achieve this goal, we formulate a
multi-objective optimization problem aimed at minimizing latency and energy
while considering given resource constraints. To facilitate decision-making for
nearly optimal computational offloading, we design an equivalent reinforcement
learning environment that accurately represents the network architecture and
the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3)
approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate
that our strategy achieves faster convergence and higher efficiency compared to
other benchmark solutions.
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