A Little Clairvoyance Is All You Need
Anupam Gupta, Haim Kaplan, Alexander Lindermayr, Jens Schl\"oter, Sorrachai Yingchareonthawornchai

TL;DR
This paper introduces a simple, constant-competitive algorithm for online job scheduling with partial job size knowledge, bridging the gap between non-clairvoyant and fully clairvoyant settings.
Contribution
It demonstrates that even limited knowledge (ε > 0) allows for a constant-competitive scheduling algorithm, providing both an algorithm and a matching lower bound.
Findings
A deterministic eil(1/psilon)-competitive algorithm for psilon > 0.
The algorithm is simple and based on the 'optimism in the face of uncertainty' principle.
A matching lower bound for randomized algorithms is established.
Abstract
We revisit the classical problem of minimizing the total flow time of jobs on a single machine in the online setting where jobs arrive over time. It has long been known that the Shortest Remaining Processing Time (SRPT) algorithm is optimal (i.e., -competitive) when the job sizes are known up-front [Schrage, 1968]. But in the non-clairvoyant setting where job sizes are revealed only when the job finishes, no algorithm can be constant-competitive [Motwani, Phillips, and Torng, 1994]. We consider the -clairvoyant setting, where , and each job's processing time becomes known once its remaining processing time equals an fraction of its processing time. This captures settings where the system user uses the initial fraction of a job's processing time to learn its true length, which it can then reveal to the algorithm.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
