# Hyper-Scalable JSQ with Sparse Feedback

## TL;DR

This paper introduces a new load balancing scheme for large-scale systems that uses sparse server feedback to significantly reduce communication overhead while maintaining near-optimal performance, outperforming existing methods in low-feedback regimes.

## Contribution

The paper proposes a novel load balancing scheme with sparse feedback that outperforms JSQ(d) and sparsified JIQ strategies, achieving vanishing wait times with minimal communication.

## Key findings

- Outperforms JSQ(d) with similar communication overhead.
- Achieves vanishing waiting time with just one message per job.
- In ultra-low feedback regimes, waiting time remains bounded in synchronous updates but diverges asynchronously.

## Abstract

Load balancing algorithms play a vital role in enhancing performance in data centers and cloud networks. Due to the massive size of these systems, scalability challenges, and especially the communication overhead associated with load balancing mechanisms, have emerged as major concerns. Motivated by these issues, we introduce and analyze a novel class of load balancing schemes where the various servers provide occasional queue updates to guide the load assignment. We show that the proposed schemes strongly outperform JSQ($d$) strategies with comparable communication overhead per job, and can achieve a vanishing waiting time in the many-server limit with just one message per job, just like the popular JIQ scheme. The proposed schemes are particularly geared however towards the sparse feedback regime with less than one message per job, where they outperform corresponding sparsified JIQ versions. We investigate fluid limits for synchronous updates as well as asynchronous exponential update intervals. The fixed point of the fluid limit is identified in the latter case, and used to derive the queue length distribution. We also demonstrate that in the ultra-low feedback regime the mean stationary waiting time tends to a constant in the synchronous case, but grows without bound in the asynchronous case.