Joint Multi-User DNN Partitioning and Computational Resource Allocation
for Collaborative Edge Intelligence
- URL: http://arxiv.org/abs/2007.09072v1
- Date: Wed, 15 Jul 2020 09:40:13 GMT
- Title: Joint Multi-User DNN Partitioning and Computational Resource Allocation
for Collaborative Edge Intelligence
- Authors: Xin Tang and Xu Chen and Liekang Zeng and Shuai Yu and Lin Chen
- Abstract summary: Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge.
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications.
We propose an algorithm called Iterative Alternating Optimization (IAO) that can achieve the optimal solution in time.
- Score: 21.55340197267767
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mobile Edge Computing (MEC) has emerged as a promising supporting
architecture providing a variety of resources to the network edge, thus acting
as an enabler for edge intelligence services empowering massive mobile and
Internet of Things (IoT) devices with AI capability. With the assistance of
edge servers, user equipments (UEs) are able to run deep neural network (DNN)
based AI applications, which are generally resource-hungry and
compute-intensive, such that an individual UE can hardly afford by itself in
real time. However the resources in each individual edge server are typically
limited. Therefore, any resource optimization involving edge servers is by
nature a resource-constrained optimization problem and needs to be tackled in
such realistic context. Motivated by this observation, we investigate the
optimization problem of DNN partitioning (an emerging DNN offloading scheme) in
a realistic multi-user resource-constrained condition that rarely considered in
previous works. Despite the extremely large solution space, we reveal several
properties of this specific optimization problem of joint multi-UE DNN
partitioning and computational resource allocation. We propose an algorithm
called Iterative Alternating Optimization (IAO) that can achieve the optimal
solution in polynomial time. In addition, we present rigorous theoretic
analysis of our algorithm in terms of time complexity and performance under
realistic estimation error. Moreover, we build a prototype that implements our
framework and conduct extensive experiments using realistic DNN models, whose
results demonstrate its effectiveness and efficiency.
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