
TL;DR
This paper explores how a single bit of advice can significantly enhance scheduling algorithms' performance, demonstrating the value of minimal, simple predictions in queue management.
Contribution
It introduces and analyzes the impact of one-bit advice in scheduling, showing its effectiveness and potential for practical implementation.
Findings
One-bit advice can model simple predictions like job size classification.
Queues with minimal advice can be analyzed using mean-field techniques.
Small advice significantly improves scheduling efficiency.
Abstract
Motivated by recent work on scheduling with predicted job sizes, we consider the performance of scheduling algorithms with minimal advice, namely a single bit. Besides demonstrating the power of very limited advice, such schemes are quite natural. In the prediction setting, one bit of advice can be used to model a simple prediction as to whether a job is "large" or "small"; that is, whether a job is above or below a given threshold. Further, one-bit advice schemes can correspond to mechanisms that tell whether to put a job at the front or the back for the queue, a limitation which may be useful in many implementation settings. Finally, queues with a single bit of advice have a simple enough state that they can be analyzed in the limiting mean-field analysis framework for the power of two choices. Our work follows in the path of recent work by showing that even small amounts of even…
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