2 research outputs found
A (1+epsilon)-Approximation for Makespan Scheduling with Precedence Constraints using LP Hierarchies
In a classical problem in scheduling, one has unit size jobs with a
precedence order and the goal is to find a schedule of those jobs on
identical machines as to minimize the makespan. It is one of the remaining four
open problems from the book of Garey & Johnson whether or not this problem is
-hard for .
We prove that for any fixed and , an LP-hierarchy lift of
the time-indexed LP with a slightly super poly-logarithmic number of rounds provides a -approximation. For example Sherali-Adams suffices as hierarchy.
This implies an algorithm that yields a -approximation in time
. The previously best approximation algorithms guarantee a -approximation in polynomial time for and
for . Our algorithm is based on a recursive scheduling
approach where in each step we reduce the correlation in form of long chains.
Our method adds to the rather short list of examples where hierarchies are
actually useful to obtain better approximation algorithms
Assignment Problems of Different-Sized Inputs in MapReduce
A MapReduce algorithm can be described by a mapping schema, which assigns
inputs to a set of reducers, such that for each required output there exists a
reducer that receives all the inputs that participate in the computation of
this output. Reducers have a capacity, which limits the sets of inputs that
they can be assigned. However, individual inputs may vary in terms of size. We
consider, for the first time, mapping schemas where input sizes are part of the
considerations and restrictions. One of the significant parameters to optimize
in any MapReduce job is communication cost between the map and reduce phases.
The communication cost can be optimized by minimizing the number of copies of
inputs sent to the reducers. The communication cost is closely related to the
number of reducers of constrained capacity that are used to accommodate
appropriately the inputs, so that the requirement of how the inputs must meet
in a reducer is satisfied. In this work, we consider a family of problems where
it is required that each input meets with each other input in at least one
reducer. We also consider a slightly different family of problems in which,
each input of a list, X, is required to meet each input of another list, Y, in
at least one reducer. We prove that finding an optimal mapping schema for these
families of problems is NP-hard, and present a bin-packing-based approximation
algorithm for finding a near optimal mapping schema.Comment: This paper is accepted in ACM Transactions on Knowledge Discovery
from Data (TKDD), August 2016. Preliminary versions of this paper have
appeared in the proceeding of DISC 2014 and BeyondMR 201