2,818 research outputs found
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
Global attraction of ODE-based mean field models with hyperexponential job sizes
Mean field modeling is a popular approach to assess the performance of large
scale computer systems. The evolution of many mean field models is
characterized by a set of ordinary differential equations that have a unique
fixed point. In order to prove that this unique fixed point corresponds to the
limit of the stationary measures of the finite systems, the unique fixed point
must be a global attractor. While global attraction was established for various
systems in case of exponential job sizes, it is often unclear whether these
proof techniques can be generalized to non-exponential job sizes. In this paper
we show how simple monotonicity arguments can be used to prove global
attraction for a broad class of ordinary differential equations that capture
the evolution of mean field models with hyperexponential job sizes. This class
includes both existing as well as previously unstudied load balancing schemes
and can be used for systems with either finite or infinite buffers. The main
novelty of the approach exists in using a Coxian representation for the
hyperexponential job sizes and a partial order that is stronger than the
componentwise partial order used in the exponential case.Comment: This paper was accepted at ACM Sigmetrics 201
On the Benefit of Information Centric Networks for Traffic Engineering
Current Internet performs traffic engineering (TE) by estimating traffic
matrices on a regular schedule, and allocating flows based upon weights
computed from these matrices. This means the allocation is based upon a guess
of the traffic in the network based on its history. Information-Centric
Networks on the other hand provide a finer-grained description of the traffic:
a content between a client and a server is uniquely identified by its name, and
the network can therefore learn the size of different content items, and
perform traffic engineering and resource allocation accordingly. We claim that
Information-Centric Networks can therefore provide a better handle to perform
traffic engineering, resulting in significant performance gain.
We present a mechanism to perform such resource allocation. We see that our
traffic engineering method only requires knowledge of the flow size (which, in
ICN, can be learned from previous data transfers) and outperforms a min-MLU
allocation in terms of response time. We also see that our method identifies
the traffic allocation patterns similar to that of min-MLU without having
access to the traffic matrix ahead of time. We show a very significant gain in
response time where min MLU is almost 50% slower than our ICN-based TE method
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