31 research outputs found
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
Stationary Distribution of a Generalized LRU-MRU Content Cache
Many different caching mechanisms have been previously proposed, exploring
different insertion and eviction policies and their performance individually
and as part of caching networks. We obtain a novel closed-form stationary
invariant distribution for a generalization of LRU and MRU caching nodes under
a reference Markov model. Numerical comparisons are made with an "Incremental
Rank Progress" (IRP a.k.a. CLIMB) and random eviction (a.k.a. random
replacement) methods under a steady-state Zipf popularity distribution. The
range of cache hit probabilities is smaller under MRU and larger under IRP
compared to LRU. We conclude with the invariant distribution for a special case
of a random-eviction caching tree-network and associated discussion
On Resource Pooling and Separation for LRU Caching
Caching systems using the Least Recently Used (LRU) principle have now become
ubiquitous. A fundamental question for these systems is whether the cache space
should be pooled together or divided to serve multiple flows of data item
requests in order to minimize the miss probabilities. In this paper, we show
that there is no straight yes or no answer to this question, depending on
complex combinations of critical factors, including, e.g., request rates,
overlapped data items across different request flows, data item popularities
and their sizes. Specifically, we characterize the asymptotic miss
probabilities for multiple competing request flows under resource pooling and
separation for LRU caching when the cache size is large.
Analytically, we show that it is asymptotically optimal to jointly serve
multiple flows if their data item sizes and popularity distributions are
similar and their arrival rates do not differ significantly; the
self-organizing property of LRU caching automatically optimizes the resource
allocation among them asymptotically. Otherwise, separating these flows could
be better, e.g., when data sizes vary significantly. We also quantify critical
points beyond which resource pooling is better than separation for each of the
flows when the overlapped data items exceed certain levels. Technically, we
generalize existing results on the asymptotic miss probability of LRU caching
for a broad class of heavy-tailed distributions and extend them to multiple
competing flows with varying data item sizes, which also validates the Che
approximation under certain conditions. These results provide new insights on
improving the performance of caching systems
Asymptotic Miss Ratio of LRU Caching with Consistent Hashing
To efficiently scale data caching infrastructure to support emerging big data
applications, many caching systems rely on consistent hashing to group a large
number of servers to form a cooperative cluster. These servers are organized
together according to a random hash function. They jointly provide a unified
but distributed hash table to serve swift and voluminous data item requests.
Different from the single least-recently-used (LRU) server that has already
been extensively studied, theoretically characterizing a cluster that consists
of multiple LRU servers remains yet to be explored. These servers are not
simply added together; the random hashing complicates the behavior. To this
end, we derive the asymptotic miss ratio of data item requests on a LRU cluster
with consistent hashing. We show that these individual cache spaces on
different servers can be effectively viewed as if they could be pooled together
to form a single virtual LRU cache space parametrized by an appropriate cache
size. This equivalence can be established rigorously under the condition that
the cache sizes of the individual servers are large. For typical data caching
systems this condition is common. Our theoretical framework provides a
convenient abstraction that can directly apply the results from the simpler
single LRU cache to the more complex LRU cluster with consistent hashing.Comment: 11 pages, 4 figure
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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