47,103 research outputs found
Online Supplement to `Efficient Simulation Resource Sharing and Allocation for Selecting the Best'
This is the online supplement to the article by the same authors, "Efficient Simulation Resource Sharing and Allocation for Selecting the Best," published in the IEEE Transactions on Automatic Control
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
Adaptive Beam-Frequency Allocation Algorithm with Position Uncertainty for Millimeter-Wave MIMO Systems
Envisioned for fifth generation (5G) systems, millimeter-wave (mmWave)
communications are under very active research worldwide. Although pencil beams
with accurate beamtracking may boost the throughput of mmWave systems, this
poses great challenges in the design of radio resource allocation for highly
mobile users. In this paper, we propose a joint adaptive beam-frequency
allocation algorithm that takes into account the position uncertainty inherent
to high mobility and/or unstable users as, e.g., Unmanned Aerial Vehicles
(UAV), for whom this is a major problem. Our proposed method provides an
optimized beamwidth selection under quality of service (QoS) requirements for
maximizing system proportional fairness, under user position uncertainty. The
rationale of our scheme is to adapt the beamwidth such that the best trade-off
among system performance (narrower beam) and robustness to uncertainty (wider
beam) is achieved. Simulation results show that the proposed method largely
enhances the system performance compared to reference algorithms, by an
appropriate adaptation of the mmWave beamwidths, even under severe
uncertainties and imperfect channel state information (CSIs).Comment: 5 pages, 6 figures, 1 table, 1 algorith
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