On the Configuration-LP for Scheduling on Unrelated Machines

TL;DR

This paper investigates the limitations of the configuration-LP in scheduling on unrelated machines, proving an integrality gap of 2 even in simplified cases, and introduces a combinatorial 2-approximation algorithm for maximizing minimum load.

Contribution

It demonstrates the integrality gap of the configuration-LP is 2 for unrelated graph balancing, and presents a simple 2-approximation for maximizing minimum load with limited machine assignments.

Findings

Configuration-LP has an integrality gap of 2 for unrelated graph balancing.

A simple combinatorial 2-approximation algorithm is optimal unless P=NP.

The problem is more complex than the restricted assignment case.

Abstract

One of the most important open problems in machine scheduling is the problem of scheduling a set of jobs on unrelated machines to minimize the makespan. The best known approximation algorithm for this problem guarantees an approximation factor of 2. It is known to be NP-hard to approximate with a better ratio than 3/2. Closing this gap has been open for over 20 years. The best known approximation factors are achieved by LP-based algorithms. The strongest known linear program formulation for the problem is the configuration-LP. We show that the configuration-LP has an integrality gap of 2 even for the special case of unrelated graph balancing, where each job can be assigned to at most two machines. In particular, our result implies that a large family of cuts does not help to diminish the integrality gap of the canonical assignment-LP. Also, we present cases of the problem which can be approximated with a better factor than 2. They constitute valuable insights for constructing an NP-hardness reduction which improves the known lower bound. Very recently Svensson studied the restricted assignment case, where each job can only be assigned to a given set of machines on which it has the same processing time. He shows that in this setting the configuration-LP has an integrality gap of 33/17<2. Hence, our result imply that the unrelated graph balancing case is significantly more complex than the restricted assignment case. Then we turn to another objective function: maximizing the minimum machine load. For the case that every job can be assigned to at most two machines we give a purely combinatorial 2-approximation algorithm which is best possible, unless P=NP. This improves on the computationally costly LP-based (2+eps)-approximation algorithm by Chakrabarty et al.