955 research outputs found
The Geometry of Scheduling
We consider the following general scheduling problem: The input consists of n
jobs, each with an arbitrary release time, size, and a monotone function
specifying the cost incurred when the job is completed at a particular time.
The objective is to find a preemptive schedule of minimum aggregate cost. This
problem formulation is general enough to include many natural scheduling
objectives, such as weighted flow, weighted tardiness, and sum of flow squared.
Our main result is a randomized polynomial-time algorithm with an approximation
ratio O(log log nP), where P is the maximum job size. We also give an O(1)
approximation in the special case when all jobs have identical release times.
The main idea is to reduce this scheduling problem to a particular geometric
set-cover problem which is then solved using the local ratio technique and
Varadarajan's quasi-uniform sampling technique. This general algorithmic
approach improves the best known approximation ratios by at least an
exponential factor (and much more in some cases) for essentially all of the
nontrivial common special cases of this problem. Our geometric interpretation
of scheduling may be of independent interest.Comment: Conference version in FOCS 201
Distributed Set Cover Approximation: Primal-Dual with Optimal Locality
This paper presents a deterministic distributed algorithm for computing an f(1+epsilon) approximation of the well-studied minimum set cover problem, for any constant epsilon>0, in O(log (f Delta)/log log (f Delta)) rounds. Here, f denotes the maximum element frequency and Delta denotes the cardinality of the largest set. This f(1+epsilon) approximation almost matches the f-approximation guarantee of standard centralized primal-dual algorithms, which is known to be essentially the best possible approximation for polynomial-time computations. The round complexity almost matches the Omega(log (Delta)/log log (Delta)) lower bound of Kuhn, Moscibroda, Wattenhofer [JACM\u2716], which holds for even f=2 and for any poly(log Delta) approximation. Our algorithm also gives an alternative way to reproduce the time-optimal 2(1+epsilon)-approximation of vertex cover, with round complexity O(log Delta/log log Delta), as presented by Bar-Yehuda, Censor-Hillel, and Schwartzman [PODC\u2717] for weighted vertex cover. Our method is quite different and it can be viewed as a locality-optimal way of performing primal-dual for the more general case of set cover. We note that the vertex cover algorithm of Bar-Yehuda et al. does not extend to set cover (when f >= 3)
How Unsplittable-Flow-Covering helps Scheduling with Job-Dependent Cost Functions
Generalizing many well-known and natural scheduling problems, scheduling with
job-specific cost functions has gained a lot of attention recently. In this
setting, each job incurs a cost depending on its completion time, given by a
private cost function, and one seeks to schedule the jobs to minimize the total
sum of these costs. The framework captures many important scheduling objectives
such as weighted flow time or weighted tardiness. Still, the general case as
well as the mentioned special cases are far from being very well understood
yet, even for only one machine. Aiming for better general understanding of this
problem, in this paper we focus on the case of uniform job release dates on one
machine for which the state of the art is a 4-approximation algorithm. This is
true even for a special case that is equivalent to the covering version of the
well-studied and prominent unsplittable flow on a path problem, which is
interesting in its own right. For that covering problem, we present a
quasi-polynomial time -approximation algorithm that yields an
-approximation for the above scheduling problem. Moreover, for
the latter we devise the best possible resource augmentation result regarding
speed: a polynomial time algorithm which computes a solution with \emph{optimal
}cost at speedup. Finally, we present an elegant QPTAS for the
special case where the cost functions of the jobs fall into at most
many classes. This algorithm allows the jobs even to have up to many
distinct release dates.Comment: 2 pages, 1 figur
A Water-Filling Primal-Dual Algorithm for Approximating Non-Linear Covering Problems
Obtaining strong linear relaxations of capacitated covering problems constitute a significant technical challenge even for simple settings. For one of the most basic cases, the Knapsack-Cover (Min-Knapsack) problem, the relaxation based on knapsack-cover inequalities has an integrality gap of 2. These inequalities are exploited in more general problems, many of which admit primal-dual approximation algorithms.
Inspired by problems from power and transport systems, we introduce a general setting in which items can be taken fractionally to cover a given demand. The cost incurred by an item is given by an arbitrary non-decreasing function of the chosen fraction. We generalize the knapsack-cover inequalities to this setting an use them to obtain a (2+?)-approximate primal-dual algorithm. Our procedure has a natural interpretation as a bucket-filling algorithm which effectively overcomes the difficulties implied by having different slopes in the cost functions. More precisely, when some superior segment of an item presents a low slope, it helps to increase the priority of inferior segments. We also present a rounding algorithm with an approximation guarantee of 2.
We generalize our algorithm to the Unsplittable Flow-Cover problem on a line, also for the setting of fractional items with non-linear costs. For this problem we obtain a (4+?)-approximation algorithm in polynomial time, almost matching the 4-approximation algorithm known for the classical setting
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