29,613 research outputs found
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
On participatory service provision at the network edge with community home gateways
Edge computing is considered as a technology to enable new types of services which operate at the network edge. There are important use cases in ambient intelligence and the Internet of Things (IoT) for edge computing driven by huge business potentials. Most of today's edge computing platforms, however, consist of proprietary gateways, which are either closed or fairly restricted to deploy any third-party services. In this paper we discuss a participatory edge computing system running on home gateways to serve as an open environment to deploy local services. We present first motivating use cases and review existing approaches and design considerations for the proposed system. Then we show our platform which materializes the principles of an open and participatory edge environment, to lower the entry barriers for service deployment at the network edge. By using containers, our platform can flexibly enable third-party services, and may serve as an infrastructure to support several application domains of ambient intelligence.Peer ReviewedPostprint (author's final draft
Developing a Resource-Constraint EdgeAI model for Surface Defect Detection
Resource constraints have restricted several EdgeAI applications to machine
learning inference approaches, where models are trained on the cloud and
deployed to the edge device. This poses challenges such as bandwidth, latency,
and privacy associated with storing data off-site for model building. Training
on the edge device can overcome these challenges by eliminating the need to
transfer data to another device for storage and model development. On-device
training also provides robustness to data variations as models can be retrained
on newly acquired data to improve performance. We, therefore, propose a
lightweight EdgeAI architecture modified from Xception, for on-device training
in a resource-constraint edge environment. We evaluate our model on a PCB
defect detection task and compare its performance against existing lightweight
models - MobileNetV2, EfficientNetV2B0, and MobileViT-XXS. The results of our
experiment show that our model has a remarkable performance with a test
accuracy of 73.45% without pre-training. This is comparable to the test
accuracy of non-pre-trained MobileViT-XXS (75.40%) and much better than other
non-pre-trained models (MobileNetV2 - 50.05%, EfficientNetV2B0 - 54.30%). The
test accuracy of our model without pre-training is comparable to pre-trained
MobileNetV2 model - 75.45% and better than pre-trained EfficientNetV2B0 model -
58.10%. In terms of memory efficiency, our model performs better than
EfficientNetV2B0 and MobileViT-XXS. We find that the resource efficiency of
machine learning models does not solely depend on the number of parameters but
also depends on architectural considerations. Our method can be applied to
other resource-constraint applications while maintaining significant
performance.Comment: Keywords: Lightweight Edge AI, Resource-constraint ML, Surface Defect
Detectio
Security challenges of small cell as a service in virtualized mobile edge computing environments
Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users
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