# Straggler Mitigation at Scale

## TL;DR

This paper analyzes the tradeoffs of using redundancy and relaunch strategies to mitigate stragglers in distributed systems, providing quantitative insights into cost and latency impacts based on empirical data.

## Contribution

It offers a comprehensive cost versus latency analysis of redundancy and relaunch techniques, including novel expressions and strategies for optimizing distributed job execution.

## Key findings

- Redundancy effectiveness depends on service time variability tail heaviness.
- Introducing redundancy after waiting can reduce costs.
- Combining redundancy with relaunching improves performance.

## Abstract

Runtime performance variability at the servers has been a major issue, hindering the predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers has been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has drawn significant attention, and numerous papers have analyzed the pain and gain of redundancy under various service models and assumptions on the runtime variability. This paper presents a cost (pain) vs. latency (gain) analysis of executing jobs of many tasks by employing replicated or erasure coded redundancy. Tail heaviness of service time variability is decisive on the pain and gain of redundancy and we quantify its effect by deriving expressions for the cost and latency. Specifically, we try to answer four questions: 1) How do replicated and coded redundancy compare in the cost vs. latency tradeoff? 2) Can we introduce redundancy after waiting some time and expect to reduce the cost? 3) Can relaunching the tasks that appear to be straggling after some time help to reduce cost and/or latency? 4) Is it effective to use redundancy and relaunching together? We validate the answers we found for each of the questions via simulations that use empirical distributions extracted from a Google cluster data.