11,948 research outputs found
Metrical Service Systems with Multiple Servers
We study the problem of metrical service systems with multiple servers
(MSSMS), which generalizes two well-known problems -- the -server problem,
and metrical service systems. The MSSMS problem is to service requests, each of
which is an -point subset of a metric space, using servers, with the
objective of minimizing the total distance traveled by the servers.
Feuerstein initiated a study of this problem by proving upper and lower
bounds on the deterministic competitive ratio for uniform metric spaces. We
improve Feuerstein's analysis of the upper bound and prove that his algorithm
achieves a competitive ratio of . In the randomized
online setting, for uniform metric spaces, we give an algorithm which achieves
a competitive ratio , beating the deterministic lower
bound of . We prove that any randomized algorithm for
MSSMS on uniform metric spaces must be -competitive. We then
prove an improved lower bound of on
the competitive ratio of any deterministic algorithm for -MSSMS, on
general metric spaces. In the offline setting, we give a pseudo-approximation
algorithm for -MSSMS on general metric spaces, which achieves an
approximation ratio of using servers. We also prove a matching
hardness result, that a pseudo-approximation with less than servers is
unlikely, even for uniform metric spaces. For general metric spaces, we
highlight the limitations of a few popular techniques, that have been used in
algorithm design for the -server problem and metrical service systems.Comment: 18 pages; accepted for publication at COCOON 201
On Randomized Memoryless Algorithms for the Weighted -server Problem
The weighted -server problem is a generalization of the -server problem
in which the cost of moving a server of weight through a distance
is . The weighted server problem on uniform spaces models
caching where caches have different write costs. We prove tight bounds on the
performance of randomized memoryless algorithms for this problem on uniform
metric spaces. We prove that there is an -competitive memoryless
algorithm for this problem, where ;
. On the other hand we also prove that no randomized memoryless
algorithm can have competitive ratio better than .
To prove the upper bound of we develop a framework to bound from
above the competitive ratio of any randomized memoryless algorithm for this
problem. The key technical contribution is a method for working with potential
functions defined implicitly as the solution of a linear system. The result is
robust in the sense that a small change in the probabilities used by the
algorithm results in a small change in the upper bound on the competitive
ratio. The above result has two important implications. Firstly this yields an
-competitive memoryless algorithm for the weighted -server problem
on uniform spaces. This is the first competitive algorithm for which is
memoryless. Secondly, this helps us prove that the Harmonic algorithm, which
chooses probabilities in inverse proportion to weights, has a competitive ratio
of .Comment: Published at the 54th Annual IEEE Symposium on Foundations of
Computer Science (FOCS 2013
Online Service with Delay
In this paper, we introduce the online service with delay problem. In this
problem, there are points in a metric space that issue service requests
over time, and a server that serves these requests. The goal is to minimize the
sum of distance traveled by the server and the total delay in serving the
requests. This problem models the fundamental tradeoff between batching
requests to improve locality and reducing delay to improve response time, that
has many applications in operations management, operating systems, logistics,
supply chain management, and scheduling.
Our main result is to show a poly-logarithmic competitive ratio for the
online service with delay problem. This result is obtained by an algorithm that
we call the preemptive service algorithm. The salient feature of this algorithm
is a process called preemptive service, which uses a novel combination of
(recursive) time forwarding and spatial exploration on a metric space. We hope
this technique will be useful for related problems such as reordering buffer
management, online TSP, vehicle routing, etc. We also generalize our results to
servers.Comment: 30 pages, 11 figures, Appeared in 49th ACM Symposium on Theory of
Computing (STOC), 201
The K-Server Dual and Loose Competitiveness for Paging
This paper has two results. The first is based on the surprising observation
that the well-known ``least-recently-used'' paging algorithm and the
``balance'' algorithm for weighted caching are linear-programming primal-dual
algorithms. This observation leads to a strategy (called ``Greedy-Dual'') that
generalizes them both and has an optimal performance guarantee for weighted
caching.
For the second result, the paper presents empirical studies of paging
algorithms, documenting that in practice, on ``typical'' cache sizes and
sequences, the performance of paging strategies are much better than their
worst-case analyses in the standard model suggest. The paper then presents
theoretical results that support and explain this. For example: on any input
sequence, with almost all cache sizes, either the performance guarantee of
least-recently-used is O(log k) or the fault rate (in an absolute sense) is
insignificant.
Both of these results are strengthened and generalized in``On-line File
Caching'' (1998).Comment: conference version: "On-Line Caching as Cache Size Varies", SODA
(1991
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