---
title: "Large-scale cluster management at Google with Borg"
description: "Borg asks how one fleet can run latency-sensitive services and opportunistic batch work without wasting the space between their peaks. Read it to see how cells, jobs, tasks, priorities, limits, reservations, placement scoring, and a continuously repaired control plane turn heterogeneous machines into shared infrastructure."
canonical_url: "https://fanout.sh/daily/2026-08-24-google-borg-cluster-management"
md_url: "https://fanout.sh/daily/2026-08-24-google-borg-cluster-management.md"
last_updated: "2026-08-24"
access: "public"
---

# Large-scale cluster management at Google with Borg

Borg asks how one fleet can run latency-sensitive services and opportunistic batch work without wasting the space between their peaks. Read it to see how cells, jobs, tasks, priorities, limits, reservations, placement scoring, and a continuously repaired control plane turn heterogeneous machines into shared infrastructure.

## Paper details

- Authors: Verma et al.

- Venue: EuroSys 2015

- Track: Systems

- Difficulty: Easy

- Reading time: 40 min

- Original paper: https://research.google/pubs/large-scale-cluster-management-at-google-with-borg/

## What you will learn

- How Borg separates a replicated control plane from per-machine Borglets and continuously reconciles desired jobs with observed task state.

- Why scheduling first filters feasible machines and then scores them, while priority, limits, and reservations make mixed workloads practical.

- What Google's measurements do and do not show about packing, reclaimed capacity, availability, isolation, and the cost of segregating workloads.

## Continue exploring

[Explore distributed task scheduling](https://fanout.sh/system/archive/distributed-task-scheduler): Continue into Fanout's scheduler archive and connect Borg's cells, placement constraints, resource reclamation, priorities, and reconciliation loop to modern cluster schedulers.

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