On the Persistent-Idle Load Distribution Policy Under Batch Arrivals and Random Service Capacity

TL;DR

This paper proves the stability of the Persistent-Idle load distribution policy under batch arrivals and random service capacities, extending its applicability and demonstrating its effectiveness through simulations.

Contribution

It extends the stability proof of PI to models with batch arrivals and random capacities, and introduces PI-Split with proven stability.

Findings

PI achieves stability in models with batch arrivals and random capacities.

PI and PI-Split outperform other policies in simulations.

Both policies maintain stability across various parameters.

Abstract

The Persistent-Idle (PI) load distribution policy was recently introduced as an appealing alternative to current low-communication load balancing techniques. In PI, servers only update the dispatcher when they become idle, and the dispatcher always sends jobs to the last server that reported being idle. PI is unique in that it does not seek to push the server queue lengths towards equalization greedily. Rather, it aggressively pulls the servers away from starvation. As a result, PI's analysis requires different tools than other load balancing approaches. So far, PI was proven to achieve the stability region for Bernoulli arrivals and deterministic and constant service capacities. Our main contribution is proving that PI achieves the stability region in a model with batch arrivals and random service capacities. Proving this result requires developing tighter bounds on quantities of interest and proving the fulfillment of a generalized version of the state-dependent drift criteria previously considered. We also present PI-Split, which allows batches to be split among idle servers and prove its stability. Finally, we conduct simulations comparing PI and PI-Split to other load balancing policies. Simulation results indicate that both achieve excellent performance for the model under consideration for a wide range of parameters.