615 research outputs found
Modeling LEAST RECENTLY USED caches with Shot Noise request processes
In this paper we analyze Least Recently Used (LRU) caches operating under the Shot Noise requests Model (SNM).
The SNM was recently proposed in [33] to better capture the main characteristics of today Video on Demand (VoD) traffic.
We investigate the validity of Che's approximation through an asymptotic analysis
of the cache eviction time. In particular, we provide a law of large numbers, a large deviation principle and a central limit theorem for the cache eviction time,
as the cache size grows large. Finally, we derive upper and lower bounds for the ``hit" probability in tandem networks of caches under Che's approximation
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
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
Temporal Locality in Today's Content Caching: Why it Matters and How to Model it
The dimensioning of caching systems represents a difficult task in the design
of infrastructures for content distribution in the current Internet. This paper
addresses the problem of defining a realistic arrival process for the content
requests generated by users, due its critical importance for both analytical
and simulative evaluations of the performance of caching systems. First, with
the aid of YouTube traces collected inside operational residential networks, we
identify the characteristics of real traffic that need to be considered or can
be safely neglected in order to accurately predict the performance of a cache.
Second, we propose a new parsimonious traffic model, named the Shot Noise Model
(SNM), that enables users to natively capture the dynamics of content
popularity, whilst still being sufficiently simple to be employed effectively
for both analytical and scalable simulative studies of caching systems.
Finally, our results show that the SNM presents a much better solution to
account for the temporal locality observed in real traffic compared to existing
approaches.Comment: 7 pages, 7 figures, Accepted for publication in ACM Computer
Communication Revie
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
201
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
Cache Miss Estimation for Non-Stationary Request Processes
The aim of the paper is to evaluate the miss probability of a Least Recently
Used (LRU) cache, when it is offered a non-stationary request process given by
a Poisson cluster point process. First, we construct a probability space using
Palm theory, describing how to consider a tagged document with respect to the
rest of the request process. This framework allows us to derive a general
integral formula for the expected number of misses of the tagged document.
Then, we consider the limit when the cache size and the arrival rate go to
infinity proportionally, and use the integral formula to derive an asymptotic
expansion of the miss probability in powers of the inverse of the cache size.
This enables us to quantify and improve the accuracy of the so-called Che
approximation
- …