1,532 research outputs found
Dataplane Specialization for High-performance OpenFlow Software Switching
OpenFlow is an amazingly expressive dataplane program-
ming language, but this expressiveness comes at a severe
performance price as switches must do excessive packet clas-
sification in the fast path. The prevalent OpenFlow software
switch architecture is therefore built on flow caching, but
this imposes intricate limitations on the workloads that can
be supported efficiently and may even open the door to mali-
cious cache overflow attacks. In this paper we argue that in-
stead of enforcing the same universal flow cache semantics
to all OpenFlow applications and optimize for the common
case, a switch should rather automatically specialize its dat-
aplane piecemeal with respect to the configured workload.
We introduce ES WITCH , a novel switch architecture that
uses on-the-fly template-based code generation to compile
any OpenFlow pipeline into efficient machine code, which
can then be readily used as fast path. We present a proof-
of-concept prototype and we demonstrate on illustrative use
cases that ES WITCH yields a simpler architecture, superior
packet processing speed, improved latency and CPU scala-
bility, and predictable performance. Our prototype can eas-
ily scale beyond 100 Gbps on a single Intel blade even with
complex OpenFlow pipelines
DHT-based distributed ALE engine in RFID Middleware
Following the “Internet of Things” concept, each object is associated with a unique identifier which will allow to retrieve information about it in large databases. In the process of managing a large amount of objects, and consequently a large amount of events from readers, without overloading the network, these events have to be filtered and aggregated. This is the aim of the Application Level Events (ALE) standard from EPCGlobal, which receives events from readers and sends a useful and well constructed report to the business application. The ALE may be connected to several hundreds of readers. As the number of readers may increase with the increase of the company, a bottleneck may appear with all readers events sent to the ALE. A solution for scalability is to distribute the ALE. In this research report, we propose an efficient way to solve this problem based on a Distributed Hash table (DHT). One role of the ALE is to insulate business application from technical concern so in our solution, we present a mechanism to distribute the ALE using Chord, a well-known peer-to-peer lookup system, and being transparent for business application. This solution is compliant with the EPCglobal existing standard, scalable, robust and transparent for other layers of the middleware. We show that the overhead generated by our solution is of 10% only in a nominal case
Handling Massive N-Gram Datasets Efficiently
This paper deals with the two fundamental problems concerning the handling of
large n-gram language models: indexing, that is compressing the n-gram strings
and associated satellite data without compromising their retrieval speed; and
estimation, that is computing the probability distribution of the strings from
a large textual source. Regarding the problem of indexing, we describe
compressed, exact and lossless data structures that achieve, at the same time,
high space reductions and no time degradation with respect to state-of-the-art
solutions and related software packages. In particular, we present a compressed
trie data structure in which each word following a context of fixed length k,
i.e., its preceding k words, is encoded as an integer whose value is
proportional to the number of words that follow such context. Since the number
of words following a given context is typically very small in natural
languages, we lower the space of representation to compression levels that were
never achieved before. Despite the significant savings in space, our technique
introduces a negligible penalty at query time. Regarding the problem of
estimation, we present a novel algorithm for estimating modified Kneser-Ney
language models, that have emerged as the de-facto choice for language modeling
in both academia and industry, thanks to their relatively low perplexity
performance. Estimating such models from large textual sources poses the
challenge of devising algorithms that make a parsimonious use of the disk. The
state-of-the-art algorithm uses three sorting steps in external memory: we show
an improved construction that requires only one sorting step thanks to
exploiting the properties of the extracted n-gram strings. With an extensive
experimental analysis performed on billions of n-grams, we show an average
improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February
2019, Article No: 2
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