2,803 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
Hierarchical Coded Caching
Caching of popular content during off-peak hours is a strategy to reduce
network loads during peak hours. Recent work has shown significant benefits of
designing such caching strategies not only to deliver part of the content
locally, but also to provide coded multicasting opportunities even among users
with different demands. Exploiting both of these gains was shown to be
approximately optimal for caching systems with a single layer of caches.
Motivated by practical scenarios, we consider in this work a hierarchical
content delivery network with two layers of caches. We propose a new caching
scheme that combines two basic approaches. The first approach provides coded
multicasting opportunities within each layer; the second approach provides
coded multicasting opportunities across multiple layers. By striking the right
balance between these two approaches, we show that the proposed scheme achieves
the optimal communication rates to within a constant multiplicative and
additive gap. We further show that there is no tension between the rates in
each of the two layers up to the aforementioned gap. Thus, both layers can
simultaneously operate at approximately the minimum rate.Comment: 31 page
Optimal Data Placement on Networks With Constant Number of Clients
We introduce optimal algorithms for the problems of data placement (DP) and
page placement (PP) in networks with a constant number of clients each of which
has limited storage availability and issues requests for data objects. The
objective for both problems is to efficiently utilize each client's storage
(deciding where to place replicas of objects) so that the total incurred access
and installation cost over all clients is minimized. In the PP problem an extra
constraint on the maximum number of clients served by a single client must be
satisfied. Our algorithms solve both problems optimally when all objects have
uniform lengths. When objects lengths are non-uniform we also find the optimal
solution, albeit a small, asymptotically tight violation of each client's
storage size by lmax where lmax is the maximum length of the objects
and some arbitrarily small positive constant. We make no assumption
on the underlying topology of the network (metric, ultrametric etc.), thus
obtaining the first non-trivial results for non-metric data placement problems
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
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