Distributed Data Placement and Content Delivery in Web Caches with Non-Metric Access Costs
Keywords: Content delivery, web caches, distributed data placement, Glauber learning dynamics, potential games, LP duality
Abstract: Motivated by applications in web caches and content delivery in
peer-to-peer networks, we consider the non-metric data placement
problem and develop distributed algorithms for computing or approximating
its optimal solutions. In this problem, the goal is to
store copies of the data points among a set of cache-capacitated
servers to minimize overall data storage and clients’ access costs.
We first show that the non-metric data placement problem in which
the access costs between servers can be arbitrary nonnegative numbers
is inapproximable up to a logarithmic factor. We then provide
a game-theoretic decomposition of the objective function and show
that a natural type of Glauber dynamics in which servers update
their cache contents with probability proportional to the utility they
receive from caching those data will converge to an optimal global
solution for a sufficiently large noise parameter. In particular, we
establish the polynomial mixing time of the Glauber dynamics for
a certain range of noise parameters. Such a game-theoretic decomposition
not only provides a good performance guarantee in terms
of content delivery but also allows the system to operate in a fully
distributed manner, hence reducing the system’s computational
load and improving its robustness to system failures. Finally, we
provide another auction-based distributed algorithm, which allows
us to approximate the optimal global solution with a performance
guarantee that depends on the ratio of the revenue vs. social welfare
obtained from the underlying auction.
Track: Web Mining and Content Analysis
Submission Guidelines Scope: Yes
Submission Guidelines Blind: Yes
Submission Guidelines Format: Yes
Submission Guidelines Limit: Yes
Submission Guidelines Authorship: Yes
Student Author: No
Submission Number: 2015
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