Queues with Small Advice
This work addresses scheduling efficiency in queueing systems for applications like job processing or network management, but it appears incremental as it builds on prior research on prediction-based scheduling.
The paper tackles the problem of scheduling with minimal advice by analyzing queues with a single bit of advice, showing that even this limited information can significantly improve scheduling performance, such as reducing job waiting times or queue lengths, though specific numerical gains are not detailed in the 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 possibly inaccurate information can greatly improve scheduling performance.