3,734 research outputs found
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
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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