2,554 research outputs found
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Computation offloading has become a popular solution to support
computationally intensive and latency-sensitive applications by transferring
computing tasks to mobile edge servers (MESs) for execution, which is known as
mobile/multi-access edge computing (MEC). To improve the MEC performance, it is
required to design an optimal offloading strategy that includes offloading
decision (i.e., whether offloading or not) and computational resource
allocation of MEC. The design can be formulated as a mixed-integer nonlinear
programming (MINLP) problem, which is generally NP-hard and its effective
solution can be obtained by performing online inference through a well-trained
deep neural network (DNN) model. However, when the system environments change
dynamically, the DNN model may lose efficacy due to the drift of input
parameters, thereby decreasing the generalization ability of the DNN model. To
address this unique challenge, in this paper, we propose a multi-head ensemble
multi-task learning (MEMTL) approach with a shared backbone and multiple
prediction heads (PHs). Specifically, the shared backbone will be invariant
during the PHs training and the inferred results will be ensembled, thereby
significantly reducing the required training overhead and improving the
inference performance. As a result, the joint optimization problem for
offloading decision and resource allocation can be efficiently solved even in a
time-varying wireless environment. Experimental results show that the proposed
MEMTL outperforms benchmark methods in both the inference accuracy and mean
square error without requiring additional training data
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
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