1,270 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
QoS-aware service continuity in the virtualized edge
5G systems are envisioned to support numerous delay-sensitive applications such as the tactile
Internet, mobile gaming, and augmented reality. Such applications impose new demands on service providers
in terms of the quality of service (QoS) provided to the end-users. Achieving these demands in mobile
5G-enabled networks represent a technical and administrative challenge. One of the solutions proposed is
to provide cloud computing capabilities at the edge of the network. In such vision, services are cloudified
and encapsulated within the virtual machines or containers placed in cloud hosts at the network access layer.
To enable ultrashort processing times and immediate service response, fast instantiation, and migration of
service instances between edge nodes are mandatory to cope with the consequences of user’s mobility.
This paper surveys the techniques proposed for service migration at the edge of the network. We focus
on QoS-aware service instantiation and migration approaches, comparing the mechanisms followed and
emphasizing their advantages and disadvantages. Then, we highlight the open research challenges still left
unhandled.publishe
Edge Offloading in Smart Grid
The energy transition supports the shift towards more sustainable energy
alternatives, paving towards decentralized smart grids, where the energy is
generated closer to the point of use. The decentralized smart grids foresee
novel data-driven low latency applications for improving resilience and
responsiveness, such as peer-to-peer energy trading, microgrid control, fault
detection, or demand response. However, the traditional cloud-based smart grid
architectures are unable to meet the requirements of the new emerging
applications such as low latency and high-reliability thus alternative
architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge
offloading can play a pivotal role for the next-generation smart grid AI
applications because it enables the efficient utilization of computing
resources and addresses the challenges of increasing data generated by IoT
devices, optimizing the response time, energy consumption, and network
performance. However, a comprehensive overview of the current state of research
is needed to support sound decisions regarding energy-related applications
offloading from cloud to fog or edge, focusing on smart grid open challenges
and potential impacts. In this paper, we delve into smart grid and
computational distribution architec-tures, including edge-fog-cloud models,
orchestration architecture, and serverless computing, and analyze the
decision-making variables and optimization algorithms to assess the efficiency
of edge offloading. Finally, the work contributes to a comprehensive
understanding of the edge offloading in smart grid, providing a SWOT analysis
to support decision making.Comment: to be submitted to journa
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