441 research outputs found
Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching
Proactive caching is an effective way to alleviate peak-hour traffic
congestion by prefetching popular contents at the wireless network edge. To
maximize the caching efficiency requires the knowledge of content popularity
profile, which however is often unavailable in advance. In this paper, we first
propose a new linear prediction model, named grouped linear model (GLM) to
estimate the future content requests based on historical data. Unlike many
existing works that assumed the static content popularity profile, our model
can adapt to the temporal variation of the content popularity in practical
systems due to the arrival of new contents and dynamics of user preference.
Based on the predicted content requests, we then propose a reinforcement
learning approach with model-free acceleration (RLMA) for online cache
replacement by taking into account both the cache hits and replacement cost.
This approach accelerates the learning process in non-stationary environment by
generating imaginary samples for Q-value updates. Numerical results based on
real-world traces show that the proposed prediction and learning based online
caching policy outperform all considered existing schemes.Comment: 6 pages, 4 figures, ICC 2018 worksho
Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems
To assess the performance of caching systems, the definition of a proper
process describing the content requests generated by users is required.
Starting from the analysis of traces of YouTube video requests collected inside
operational networks, we identify the characteristics of real traffic that need
to be represented and those that instead can be safely neglected. Based on our
observations, we introduce a simple, parsimonious traffic model, named Shot
Noise Model (SNM), that allows us to capture temporal and geographical locality
of content popularity. The SNM is sufficiently simple to be effectively
employed in both analytical and scalable simulative studies of caching systems.
We demonstrate this by analytically characterizing the performance of the LRU
caching policy under the SNM, for both a single cache and a network of caches.
With respect to the standard Independent Reference Model (IRM), some
paradigmatic shifts, concerning the impact of various traffic characteristics
on cache performance, clearly emerge from our results.Comment: 14 pages, 11 Figures, 2 Appendice
Adaptive TTL-Based Caching for Content Delivery
Content Delivery Networks (CDNs) deliver a majority of the user-requested
content on the Internet, including web pages, videos, and software downloads. A
CDN server caches and serves the content requested by users. Designing caching
algorithms that automatically adapt to the heterogeneity, burstiness, and
non-stationary nature of real-world content requests is a major challenge and
is the focus of our work. While there is much work on caching algorithms for
stationary request traffic, the work on non-stationary request traffic is very
limited. Consequently, most prior models are inaccurate for production CDN
traffic that is non-stationary.
We propose two TTL-based caching algorithms and provide provable guarantees
for content request traffic that is bursty and non-stationary. The first
algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic
approximation approach. Given a feasible target hit rate, we show that the hit
rate of d-TTL converges to its target value for a general class of bursty
traffic that allows Markov dependence over time and non-stationary arrivals.
The second algorithm called f-TTL uses two caches, each with its own TTL. The
first-level cache adaptively filters out non-stationary traffic, while the
second-level cache stores frequently-accessed stationary traffic. Given
feasible targets for both the hit rate and the expected cache size, f-TTL
asymptotically achieves both targets. We implement d-TTL and f-TTL and evaluate
both algorithms using an extensive nine-day trace consisting of 500 million
requests from a production CDN server. We show that both d-TTL and f-TTL
converge to their hit rate targets with an error of about 1.3%. But, f-TTL
requires a significantly smaller cache size than d-TTL to achieve the same hit
rate, since it effectively filters out the non-stationary traffic for
rarely-accessed objects
Catalog Dynamics: Impact of Content Publishing and Perishing on the Performance of a LRU Cache
The Internet heavily relies on Content Distribution Networks and transparent
caches to cope with the ever-increasing traffic demand of users. Content,
however, is essentially versatile: once published at a given time, its
popularity vanishes over time. All requests for a given document are then
concentrated between the publishing time and an effective perishing time.
In this paper, we propose a new model for the arrival of content requests,
which takes into account the dynamical nature of the content catalog. Based on
two large traffic traces collected on the Orange network, we use the
semi-experimental method and determine invariants of the content request
process. This allows us to define a simple mathematical model for content
requests; by extending the so-called "Che approximation", we then compute the
performance of a LRU cache fed with such a request process, expressed by its
hit ratio. We numerically validate the good accuracy of our model by comparison
to trace-based simulation.Comment: 13 Pages, 9 figures. Full version of the article submitted to the ITC
2014 conference. Small corrections in the appendix from the previous versio
Optimal Caching and Routing in Hybrid Networks
Hybrid networks consisting of MANET nodes and cellular infrastructure have
been recently proposed to improve the performance of military networks. Prior
work has demonstrated the benefits of in-network content caching in a wired,
Internet context. We investigate the problem of developing optimal routing and
caching policies in a hybrid network supporting in-network caching with the
goal of minimizing overall content-access delay. Here, needed content may
always be accessed at a back-end server via the cellular infrastructure;
alternatively, content may also be accessed via cache-equipped "cluster" nodes
within the MANET. To access content, MANET nodes must thus decide whether to
route to in-MANET cluster nodes or to back-end servers via the cellular
infrastructure; the in-MANET cluster nodes must additionally decide which
content to cache. We model the cellular path as either i) a
congestion-insensitive fixed-delay path or ii) a congestion-sensitive path
modeled as an M/M/1 queue. We demonstrate that under the assumption of
stationary, independent requests, it is optimal to adopt static caching (i.e.,
to keep a cache's content fixed over time) based on content popularity. We also
show that it is optimal to route to in-MANET caches for content cached there,
but to route requests for remaining content via the cellular infrastructure for
the congestion-insensitive case and to split traffic between the in-MANET
caches and cellular infrastructure for the congestion-sensitive case. We
develop a simple distributed algorithm for the joint routing/caching problem
and demonstrate its efficacy via simulation.Comment: submitted to Milcom 201
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