1,114 research outputs found
D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection
Within the framework of a Device-to-Device (D2D) data offloading system for
cellular networks, we propose a Content Delivery Management System (CDMS) in
which the instant for transmitting a content to a requesting node, through a
D2D communication, is selected to minimize the energy consumption required for
transmission. The proposed system is particularly fit to highly dynamic
scenarios, such as vehicular networks, where the network topology changes at a
rate which is comparable with the order of magnitude of the delay tolerance. We
present an analytical framework able to predict the system performance, in
terms of energy consumption, using tools from the theory of point processes,
validating it through simulations, and provide a thorough performance
evaluation of the proposed CDMS, in terms of energy consumption and spectrum
use. Our performance analysis compares the energy consumption and spectrum use
obtained with the proposed scheme with the performance of two benchmark
systems. The first one is a plain classic cellular scheme, the second is a D2D
data offloading scheme (that we proposed in previous works) in which the D2D
transmissions are performed as soon as there is a device with the required
content within the maximum D2D transmission range..
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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