2,047 research outputs found
Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams
We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. We present the general architecture for such a system and an application to classification. This architecture is an instance of the general wrapper idea allowing us to reuse standard batch learning algorithms in an inherently incremental learning environment. By artificially generating data sources we demonstrate that a hierarchy containing a mixture of models is able to adapt over time to the source of the data. In these experiments the hierarchies use an elementary performance based replacement policy and unweighted voting for making classification decisions
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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Impact of traffic mix on caching performance in a content-centric network
For a realistic traffic mix, we evaluate the hit rates attained in a
two-layer cache hierarchy designed to reduce Internet bandwidth requirements.
The model identifies four main types of content, web, file sharing, user
generated content and video on demand, distinguished in terms of their traffic
shares, their population and object sizes and their popularity distributions.
Results demonstrate that caching VoD in access routers offers a highly
favorable bandwidth memory tradeoff but that the other types of content would
likely be more efficiently handled in very large capacity storage devices in
the core. Evaluations are based on a simple approximation for LRU cache
performance that proves highly accurate in relevant configurations
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
Towards a Domain Specific Language for a Scene Graph based Robotic World Model
Robot world model representations are a vital part of robotic applications.
However, there is no support for such representations in model-driven
engineering tool chains. This work proposes a novel Domain Specific Language
(DSL) for robotic world models that are based on the Robot Scene Graph (RSG)
approach. The RSG-DSL can express (a) application specific scene
configurations, (b) semantic scene structures and (c) inputs and outputs for
the computational entities that are loaded into an instance of a world model.Comment: Presented at DSLRob 2013 (arXiv:cs/1312.5952
Cost-aware caching: optimizing cache provisioning and object placement in ICN
Caching is frequently used by Internet Service Providers as a viable
technique to reduce the latency perceived by end users, while jointly
offloading network traffic. While the cache hit-ratio is generally considered
in the literature as the dominant performance metric for such type of systems,
in this paper we argue that a critical missing piece has so far been neglected.
Adopting a radically different perspective, in this paper we explicitly account
for the cost of content retrieval, i.e. the cost associated to the external
bandwidth needed by an ISP to retrieve the contents requested by its customers.
Interestingly, we discover that classical cache provisioning techniques that
maximize cache efficiency (i.e., the hit-ratio), lead to suboptimal solutions
with higher overall cost. To show this mismatch, we propose two optimization
models that either minimize the overall costs or maximize the hit-ratio,
jointly providing cache sizing, object placement and path selection. We
formulate a polynomial-time greedy algorithm to solve the two problems and
analytically prove its optimality. We provide numerical results and show that
significant cost savings are attainable via a cost-aware design
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