Maui Scheduler

1.1 Value of a Batch System

Batch systems provide a mechanism for submitting, launching, and tracking jobs on a shared resource. These services fullfil one of the major responsibilities of a batch system, providing centralized access to distributed resources. This greatly simplifies the use of the cluster's distributed resources allowing users a 'single system image' in terms of the management of their jobs and the aggregate compute resources available. However, batch systems must do much more than provide a global view of the cluster. As with many shared systems, complexities arise when attempting to utilize compute resources in a fair and effective manner. These complexities can lead to poor performance and significant inequalities in usage. With a batch system, a scheduler is assigned the job of determining, when, where, and how jobs are run so as to maximize the output of the cluster. These decisions are broken into three primary areas.

1.1.1 Traffic Control

A scheduler is responsible for preventing jobs from interfering with each other. If jobs are allowed to contend for resources, they will generally decrease the performance of the cluster, delay the execution of these jobs, and possibly cause one or more of the jobs to fail. The scheduler is responsible for internally tracking and dedicating requested resources to a job, thus preventing use of these resources by other jobs.

1.1.2 Mission Policies

When clusters or other HPC platforms are created, they are typically created for one or more specific purposes. These purposes, or mission goals, often define various rules about how the system should be used and who or what will be allowed to use it. To be effective, a scheduler must provide a suite of policies which allow a site to map site mission policies into scheduling behavior.

1.1.3 Optimizations

The compute power of a cluster is a limited resource and over time, demand will inevitably exceed supply. Intelligent scheduling decisions can significantly improve the effectiveness of the cluster resulting in more jobs being run and quicker job turnaround. Subject to the constraints of the traffic control and mission policies, it is the job of the scheduler to use whatever freedom is available to schedule jobs in such a manner so as to maximize cluster performance.