1,042 research outputs found
Cooperative announcement-based caching for video-on-demand streaming
Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth
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
A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints
We consider caching in cellular networks in which each base station is
equipped with a cache that can store a limited number of files. The popularity
of the files is known and the goal is to place files in the caches such that
the probability that a user at an arbitrary location in the plane will find the
file that she requires in one of the covering caches is maximized.
We develop distributed asynchronous algorithms for deciding which contents to
store in which cache. Such cooperative algorithms require communication only
between caches with overlapping coverage areas and can operate in asynchronous
manner. The development of the algorithms is principally based on an
observation that the problem can be viewed as a potential game. Our basic
algorithm is derived from the best response dynamics. We demonstrate that the
complexity of each best response step is independent of the number of files,
linear in the cache capacity and linear in the maximum number of base stations
that cover a certain area. Then, we show that the overall algorithm complexity
for a discrete cache placement is polynomial in both network size and catalog
size. In practical examples, the algorithm converges in just a few iterations.
Also, in most cases of interest, the basic algorithm finds the best Nash
equilibrium corresponding to the global optimum. We provide two extensions of
our basic algorithm based on stochastic and deterministic simulated annealing
which find the global optimum.
Finally, we demonstrate the hit probability evolution on real and synthetic
networks numerically and show that our distributed caching algorithm performs
significantly better than storing the most popular content, probabilistic
content placement policy and Multi-LRU caching policies.Comment: 24 pages, 9 figures, presented at SIGMETRICS'1
Optimal Content Placement for En-Route Web Caching
This paper studies the optimal placement of web files for en-route web caching. It is shown that existing placement policies are all solving restricted partial problems of the file placement problem, and therefore give only sub-optimal solutions. A dynamic programming algorithm of low complexity which computes the optimal solution is presented. It is shown both analytically and experimentally that the file-placement solution output by our algorithm outperforms existing en-route caching policies. The optimal placement of web files can be implemented with a reasonable level of cache coordination and management overhead for en-route caching; and importantly, it can be achieved with or without using data prefetching
Efficient Proactive Caching for Supporting Seamless Mobility
We present a distributed proactive caching approach that exploits user
mobility information to decide where to proactively cache data to support
seamless mobility, while efficiently utilizing cache storage using a congestion
pricing scheme. The proposed approach is applicable to the case where objects
have different sizes and to a two-level cache hierarchy, for both of which the
proactive caching problem is hard. Additionally, our modeling framework
considers the case where the delay is independent of the requested data object
size and the case where the delay is a function of the object size. Our
evaluation results show how various system parameters influence the delay gains
of the proposed approach, which achieves robust and good performance relative
to an oracle and an optimal scheme for a flat cache structure.Comment: 10 pages, 9 figure
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
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