10,187 research outputs found
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
Exact Analysis of TTL Cache Networks: The Case of Caching Policies driven by Stopping Times
TTL caching models have recently regained significant research interest,
largely due to their ability to fit popular caching policies such as LRU. This
paper advances the state-of-the-art analysis of TTL-based cache networks by
developing two exact methods with orthogonal generality and computational
complexity. The first method generalizes existing results for line networks
under renewal requests to the broad class of caching policies whereby evictions
are driven by stopping times. The obtained results are further generalized,
using the second method, to feedforward networks with Markov arrival processes
(MAP) requests. MAPs are particularly suitable for non-line networks because
they are closed not only under superposition and splitting, as known, but also
under input-output caching operations as proven herein for phase-type TTL
distributions. The crucial benefit of the two closure properties is that they
jointly enable the first exact analysis of feedforward networks of TTL caches
in great generality
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
A unified approach to the performance analysis of caching systems
We propose a unified methodology to analyse the performance of caches (both
isolated and interconnected), by extending and generalizing a decoupling
technique originally known as Che's approximation, which provides very accurate
results at low computational cost. We consider several caching policies, taking
into account the effects of temporal locality. In the case of interconnected
caches, our approach allows us to do better than the Poisson approximation
commonly adopted in prior work. Our results, validated against simulations and
trace-driven experiments, provide interesting insights into the performance of
caching systems.Comment: in ACM TOMPECS 20016. Preliminary version published at IEEE Infocom
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