12 research outputs found
A Literature Survey of Cooperative Caching in Content Distribution Networks
Content distribution networks (CDNs) which serve to deliver web objects
(e.g., documents, applications, music and video, etc.) have seen tremendous
growth since its emergence. To minimize the retrieving delay experienced by a
user with a request for a web object, caching strategies are often applied -
contents are replicated at edges of the network which is closer to the user
such that the network distance between the user and the object is reduced. In
this literature survey, evolution of caching is studied. A recent research
paper [15] in the field of large-scale caching for CDN was chosen to be the
anchor paper which serves as a guide to the topic. Research studies after and
relevant to the anchor paper are also analyzed to better evaluate the
statements and results of the anchor paper and more importantly, to obtain an
unbiased view of the large scale collaborate caching systems as a whole.Comment: 5 pages, 5 figure
Modeling and Analysis of Content Caching in Wireless Small Cell Networks
Network densification with small cell base stations is a promising solution
to satisfy future data traffic demands. However, increasing small cell base
station density alone does not ensure better users quality-of-experience and
incurs high operational expenditures. Therefore, content caching on different
network elements has been proposed as a mean of offloading he backhaul by
caching strategic contents at the network edge, thereby reducing latency. In
this paper, we investigate cache-enabled small cells in which we model and
characterize the outage probability, defined as the probability of not
satisfying users requests over a given coverage area. We analytically derive a
closed form expression of the outage probability as a function of
signal-to-interference ratio, cache size, small cell base station density and
threshold distance. By assuming the distribution of base stations as a Poisson
point process, we derive the probability of finding a specific content within a
threshold distance and the optimal small cell base station density that
achieves a given target cache hit probability. Furthermore, simulation results
are performed to validate the analytical model.Comment: accepted for publication, IEEE ISWCS 201
Learning-Based Optimization of Cache Content in a Small Cell Base Station
Optimal cache content placement in a wireless small cell base station (sBS)
with limited backhaul capacity is studied. The sBS has a large cache memory and
provides content-level selective offloading by delivering high data rate
contents to users in its coverage area. The goal of the sBS content controller
(CC) is to store the most popular contents in the sBS cache memory such that
the maximum amount of data can be fetched directly form the sBS, not relying on
the limited backhaul resources during peak traffic periods. If the popularity
profile is known in advance, the problem reduces to a knapsack problem.
However, it is assumed in this work that, the popularity profile of the files
is not known by the CC, and it can only observe the instantaneous demand for
the cached content. Hence, the cache content placement is optimised based on
the demand history. By refreshing the cache content at regular time intervals,
the CC tries to learn the popularity profile, while exploiting the limited
cache capacity in the best way possible. Three algorithms are studied for this
cache content placement problem, leading to different exploitation-exploration
trade-offs. We provide extensive numerical simulations in order to study the
time-evolution of these algorithms, and the impact of the system parameters,
such as the number of files, the number of users, the cache size, and the
skewness of the popularity profile, on the performance. It is shown that the
proposed algorithms quickly learn the popularity profile for a wide range of
system parameters.Comment: Accepted to IEEE ICC 2014, Sydney, Australia. Minor typos corrected.
Algorithm MCUCB correcte
Truth-Telling Mechanism for Two-Way Relay Selection for Secrecy Communications With Energy-Harvesting Revenue
This paper brings the novel idea of paying the utility to the winning agents in terms of some physical entity in cooperative communications. Our setting is a secret two-way communication channel where two transmitters exchange information in the presence of an eavesdropper. The relays are selected from a set of interested parties, such that the secrecy sum rate is maximized. In return, the selected relay nodes' energy harvesting requirements will be fulfilled up to a certain threshold through their own payoff so that they have the natural incentive to be selected and involved in the communication. However, relays may exaggerate their private information in order to improve their chance to be selected. Our objective is to develop a mechanism for relay selection that enforces them to reveal the truth since otherwise they may be penalized. We also propose a joint cooperative relay beamforming and transmit power optimization scheme based on an alternating optimization approach. Note that the problem is highly non-convex, since the objective function appears as a product of three correlated Rayleigh quotients. While a common practice in the existing literature is to optimize the relay beamforming vector for given transmit power via rank relaxation, we propose a second-order cone programming-based approach in this paper, which requires a significantly lower computational task. The performance of the incentive control mechanism and the optimization algorithm has bee