16 research outputs found
Stochastic Dynamic Cache Partitioning for Encrypted Content Delivery
In-network caching is an appealing solution to cope with the increasing
bandwidth demand of video, audio and data transfer over the Internet.
Nonetheless, an increasing share of content delivery services adopt encryption
through HTTPS, which is not compatible with traditional ISP-managed approaches
like transparent and proxy caching. This raises the need for solutions
involving both Internet Service Providers (ISP) and Content Providers (CP): by
design, the solution should preserve business-critical CP information (e.g.,
content popularity, user preferences) on the one hand, while allowing for a
deeper integration of caches in the ISP architecture (e.g., in 5G femto-cells)
on the other hand.
In this paper we address this issue by considering a content-oblivious
ISP-operated cache. The ISP allocates the cache storage to various content
providers so as to maximize the bandwidth savings provided by the cache: the
main novelty lies in the fact that, to protect business-critical information,
ISPs only need to measure the aggregated miss rates of the individual CPs and
do not need to be aware of the objects that are requested, as in classic
caching. We propose a cache allocation algorithm based on a perturbed
stochastic subgradient method, and prove that the algorithm converges close to
the allocation that maximizes the overall cache hit rate. We use extensive
simulations to validate the algorithm and to assess its convergence rate under
stationary and non-stationary content popularity. Our results (i) testify the
feasibility of content-oblivious caches and (ii) show that the proposed
algorithm can achieve within 10\% from the global optimum in our evaluation
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
Impact of Traffic Characteristics on Request Aggregation in an NDN Router
The paper revisits the performance evaluation of caching in a Named Data
Networking (NDN) router where the content store (CS) is supplemented by a
pending interest table (PIT). The PIT aggregates requests for a given content
that arrive within the download delay and thus brings an additional reduction
in upstream bandwidth usage beyond that due to CS hits. We extend prior work on
caching with non-zero download delay (non-ZDD) by proposing a novel
mathematical framework that is more easily applicable to general traffic models
and by considering alternative cache insertion policies. Specifically we
evaluate the use of an LRU filter to improve CS hit rate performance in this
non-ZDD context. We also consider the impact of time locality in demand due to
finite content lifetimes. The models are used to quantify the impact of the PIT
on upstream bandwidth reduction, demonstrating notably that this is significant
only for relatively small content catalogues or high average request rate per
content. We further explore how the effectiveness of the filter with finite
content lifetimes depends on catalogue size and traffic intensity
Timelines are Publisher-Driven Caches: Analyzing and Shaping Timeline Networks
International audienceCache networks are one of the building blocks of information centric networks (ICNs). Most of the recent work on cache networks has focused on networks of request driven caches, which are populated based on users requests for content generated by publishers. However, user generated content still poses the most pressing challenges. For such content time-lines are the de facto sharing solution. In this paper, we establish a connection between time-lines and publisher-driven caches. We propose simple models and metrics to analyze publisher-driven caches, allowing for variable-sized objects. Then, we design two efficient algorithms for timeline workload shaping, leveraging admission and price control in order, for instance, to aid service providers to attain prescribed service level agreements
Implicit Coordination of Caches in Small Cell Networks under Unknown Popularity Profiles
We focus on a dense cellular network, in which a limited-size cache is
available at every Base Station (BS). In order to optimize the overall
performance of the system in such scenario, where a significant fraction of the
users is covered by several BSs, a tight coordination among nearby caches is
needed. To this end, this pape introduces a class of simple and fully
distributed caching policies, which require neither direct communication among
BSs, nor a priori knowledge of content popularity. Furthermore, we propose a
novel approximate analytical methodology to assess the performance of
interacting caches under such policies. Our approach builds upon the well known
characteristic time approximation and provides predictions that are
surprisingly accurate (hardly distinguishable from the simulations) in most of
the scenarios. Both synthetic and trace-driven results show that the our
caching policies achieve excellent performance (in some cases provably
optimal). They outperform state-of-the-art dynamic policies for interacting
caches, and, in some cases, also the greedy content placement, which is known
to be the best performing polynomial algorithm under static and perfectly-known
content popularity profiles