Borg Cluster Management Overview

A numerical value assigned to a job, determining its access to resources. Higher priority jobs can preempt lower priority jobs.

The act of interrupting and terminating a lower-priority job to allocate resources to a higher-priority job.

Categories of job priorities, for example, monitoring or production, to manage resource allocation.

A limit on the resources (e.g., CPU, memory) a job can use. It prevents a single job from consuming disproportionate resources.

A service that provides stable names for tasks within the Borg system. These names are used to locate tasks.

Tasks in Borg are often relocated to different machines to provide efficient resource allocation.

Borg monitors task size and health information in Chubby, to improve request routing.

A naming convention for identifying jobs in Borg, including cell name, job name, and task number.

Efficiently using Google's machine fleet (a significant financial investment) to achieve cost savings through increased utilization.

Failures spreading across different parts of the system, such as machines, racks, and power domains.

The rate at which tasks are interrupted, which is limited by Borg policies to prevent system instability.

Separating production (prod) and non-production (non-prod) workloads into different cells (groups of machines).

The increase in machines required when segregating workloads (estimated as a percentage of minimum machines required for single-cell operation).

A group of machines used to run a specific workload or task.

The additional resources required for separating workloads into different cells (a percentage).

Represents the percentage of cells, showing the proportion of tasks/activities in each cell.

Upper bounds on resources (CPU, RAM, disk) each task can use.

Tasks request or use more resources than needed.

The maximum amount of resources a user is allowed to consume.

The process of recovering or freeing up unused resources.

The time a task waits to access a resource (CPU).

Percentage of time a CPU is actively processing tasks.

Tasks needing quick response (e.g., interacting with users).

Tasks that can take longer without impacting users.

A component of Borg that dynamically adjusts resource caps for tasks, like memory, disk, CPU, and network bandwidth. It ensures that tasks don't hog resources and allows for efficient resource allocation.

A sandbox-like environment created using Linux cgroups, isolating tasks from each other and managing resource usage. Each task gets its own container with limits on resources like CPU, memory, and disk.

Limits imposed on the amount of resources a task can consume, like CPU time, memory, or network bandwidth. These help prevent a single task from monopolizing resources.

The Linux kernel scheduler responsible for distributing CPU time fairly among tasks. Borg optimizes CFS, ensuring that high-priority tasks get more CPU time.

Tasks requiring low latency (fast response times), for example, web server requests. They are prioritized by Borg to ensure responsiveness.

A system design that is aware of the non-uniform memory access architecture of modern processors. It optimizes thread placement and resource allocation to minimize memory access latency.

A system for scheduling short-lived batch jobs in large-scale server clusters, aiming for high throughput.

Running lower-priority background tasks when resources are available, increasing overall utilization.

A tool in Apollo that predicts task starting times based on their size and resource requirements.

A cluster manager that splits resource management and placement functions between a central manager and multiple frameworks.

A resource allocation strategy initially developed for Mesos, prioritizing and managing quotas.

A Hadoop-centric cluster manager, where each application has a manager that negotiates for resources.

A cluster manager designed for data analysis workloads, with a central FuxiMaster for resource management.

Borg aims to make debugging information readily available to all users, promoting self-help and troubleshooting. This approach, despite being more complex, prioritizes user empowerment and efficiency.

To manage the vast amount of data, Borg provides various levels of user interfaces and debugging tools, allowing users to pinpoint anomalous events and analyze detailed event logs.

Kubernetes replicates many of Borg's introspection features by providing tools like cAdvisor for resource monitoring and log aggregation using Elasticsearch and Kibana, enabling detailed system analysis.

Kubernetes uses a unified mechanism for all components to record events, like pod scheduling, container status, or failures. This centralized event logging provides a comprehensive view of system activity.

The 'master' is the central component of the distributed Borg system, responsible for orchestration, resource allocation, and managing the overall system state.

Borg prioritizes surfacing debugging information to all users, rather than hiding it, encouraging self-sufficiency and empowering users to troubleshoot independently.

Borg's design emphasizes self-help debugging, making it easier for users to diagnose and fix problems, freeing up resources for more complex issues.

Adequate debugging tools are essential for any large-scale distributed system, as they provide crucial insights into system behavior and facilitate effective problem-solving.

A collection of cells, typically housed within a single datacenter building, representing a physical grouping of machines.

A unit of work consisting of one or more tasks, defined by its name, owner, and constraints specifying required resources.

A single unit of execution within a job, running as a set of Linux processes inside a container on a machine, representing a specific piece of work.

Machines within a cell can have diverse characteristics (CPU, RAM, disk, network, etc.), offering flexibility for different workloads.

Borg isolates users from machine heterogeneity, allocating resources, installing programs, and managing tasks to ensure efficient execution.

Requirements or preferences specified for a job, limiting the types of machines its tasks can run on based on attributes like processor architecture or OS version.

Borg primarily avoids virtual machines (VMs) for efficiency, opting for direct containerized execution of tasks on bare metal machines.

Jobs in Borg progress through different stages, beginning with 'Pending', transitioning to 'Running', and finally ending in 'Dead' or 'Lost'. Users can trigger transitions like 'submit', 'kill', and 'update'.

Users can modify running jobs by pushing new job configurations to Borg, leading to a 'update' transition. This enables lightweight, non-atomic transactions that allow for reconfiguration and can be undone until committed.

Borg allows for 'update' operations that modify running tasks, leading to disruptions. To prevent excessive disruption, Borg imposes limits on the number of tasks that can be interrupted at a time.

Jobs are collections of tasks. A single job can contain numerous tasks executing in parallel. Users can trigger actions like 'submit', 'kill', and 'update’ for both jobs and individual tasks.

When an update causes a task to no longer fit on its current machine, Borg will stop the task and reschedule it on a suitable machine.

A higher-priority task can interrupt and terminate (preempt) a lower-priority task to gain access to its resources.

Before a task is preempted, it can be notified with a SIGTERM signal, allowing time to gracefully shut down and save its state.

A reserved set of resources on a machine, dedicated for one or more tasks, even if unused.

A group of allocs that reserves resources across multiple machines.

A mechanism that decides which jobs are allowed to run based on their resource requirements and the available resources.

A high priority level for critical jobs that need to run smoothly, often used for production applications.

Tasks in Borg can be moved to different machines to optimize resource utilization and ensure efficient execution.

A group of machines in Borg, typically dedicated to a specific workload or task, providing a container for resource management.