Delay-Optimal Forwarding and Computation Offloading for Service Chain Tasks
CoRR(2024)
摘要
Emerging edge computing paradigms enable heterogeneous devices to collaborate
on complex computation applications. However, for congestible links and
computing units, delay-optimal forwarding and offloading for service chain
tasks (e.g., DNN with vertical split) in edge computing networks remains an
open problem. In this paper, we formulate the service chain forwarding and
offloading problem with arbitrary topology and heterogeneous
transmission/computation capability, and aim to minimize the aggregated network
cost. We consider congestion-aware nonlinear cost functions that cover various
performance metrics and constraints, such as average queueing delay with
limited processor capacity. We solve the non-convex optimization problem
globally by analyzing the KKT condition and proposing a sufficient condition
for optimality. We then propose a distributed algorithm that converges to the
global optimum. The algorithm adapts to changes in input rates and network
topology, and can be implemented as an online algorithm. Numerical evaluation
shows that our method significantly outperforms baselines in multiple network
instances, especially in congested scenarios.
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