Web caches can not only reduce network traffic and downloading latency, but can also affect the distribution of web traffic over the network through cost-aware caching. This paper introduces GreedyDual-Size, which incorporates locality with cost and size concerns in a simple and non-parameterized fashion for high performance. Trace-driven simulations show that with the appropriate cost definition, GreedyDual-Size outperforms existing web cache replacement algorithms in many aspects, including hit ratios, latency reduction and network cost reduction. In addition, GreedyDual-Size can potentially improve the performance of main-memory caching of Web documents.

/pdf/cost-aware-www-proxy-caching-algorithms-234kttt5bt.pdf

Cost-aware WWW proxy caching algorithms

Note: Professor Pach's number: [045]; Also in: Facsimile edition: World Classics in Mathematics Series, vol. 28, China Science Press, Beijing, 2006. Mimeographed from 100 Research Problems in Discrete Geometry (with W. O. J. Moser). Reference DCG-BOOK-2008-001 URL: http://www.math.nyu.edu/~pach/research_problems.html Record created on 2008-11-18, modified on 2017-05-12

Research Problems in Discrete Geometry

This paper provides a novel technique for the analysis of randomized algorithms for optimization problems on metric spaces, by relating the randomized performance ratio for any, metric space to the randomized performance ratio for a set of "simple" metric spaces. We define a notion of a set of metric spaces that probabilistically-approximates another metric space. We prove that any metric space can be probabilistically-approximated by hierarchically well-separated trees (HST) with a polylogarithmic distortion. These metric spaces are "simple" as being: (1) tree metrics; (2) natural for applying a divide-and-conquer algorithmic approach. The technique presented is of particular interest in the context of on-line computation. A large number of on-line algorithmic problems, including metrical task systems, server problems, distributed paging, and dynamic storage rearrangement are defined in terms of some metric space. Typically for these problems, there are linear lower bounds on the competitive ratio of deterministic algorithms. Although randomization against an oblivious adversary has the potential of overcoming these high ratios, very little progress has been made in the analysis. We demonstrate the use of our technique by obtaining substantially improved results for two different on-line problems.

Probabilistic approximation of metric spaces and its algorithmic applications

https://www.cs.ucla.edu/~awm/cs289/competitive-paging.pdf

Competitive paging algorithms

In practice, almost all dynamic systems require decisions to be made on-line, without full knowledge of their future impact on the system. A general model for the processing of sequences of tasks is introduced, and a general on-line decision algorithm is developed. It is shown that, for an important class of special cases, this algorithm is optimal among all on-line algorithms.Specifically, a task system (S,d) for processing sequences of tasks consists of a set S of states and a cost matrix d where d(i, j is the cost of changing from state i to state j (we assume that d satisfies the triangle inequality and all diagonal entries are 0). The cost of processing a given task depends on the state of the system. A schedule for a sequence T1, T2,…, Tk of tasks is a sequence s1, s2,…,sk of states where si is the state in which Ti is processed; the cost of a schedule is the sum of all task processing costs and the state transition costs incurred.An on-line scheduling algorithm is one that chooses si only knowing T1T2…Ti. Such an algorithm is w-competitive if, on any input task sequence, its cost is within an additive constant of w times the optimal offline schedule cost. The competitive ratio w(S, d) is the infimum w for which there is a w-competitive on-line scheduling algorithm for (S,d). It is shown that w(S, d) = 2|S|–1 for every task system in which d is symmetric, and w(S, d) = O(|S|2) for every task system. Finally, randomized on-line scheduling algorithms are introduced. It is shown that for the uniform task system (in which d(i,j) = 1 for all i,j), the expected competitive ratio w¯(S,d) = O(log|S|).

/pdf/an-optimal-on-line-algorithm-for-metrical-task-system-420hjczzki.pdf

An optimal on-line algorithm for metrical task system

In the k-server problem, one must choose how k mobile servers will serve each of a sequence of requests, making decisions in an online manner. An optimal deterministic online strategy is exhibited when the requests fall on the real line. For the weighted-cache problem, in which the cost of moving to x from any other point is $w( x )$, the weight of x, an optimal deterministic algorithm is also provided. The nonexistence of competitive algorithms for the asymmetric two-server problem and of memoryless algorithms for the weighted-cache problem is proved. A fast algorithm for oflline computing of an optimal schedule is given, and it is shown that finding an optimal offline schedule is at least as hard as the assignment problem.

T. H. Payne

Papers

New results on server problems

New results on server problems

A linear-time algorithm for drawing a planar graph on a grid

title=New Results on Server Problems.