From cloudera old article ( Hadoop 1.x):
Once a queue is selected, the Scheduler picks a job in the queue. Jobs are sorted based on when they're submitted and their priorities (if the queue supports priorities).
Jobs are considered in order, and a job is selected if its user is within the user-quota for the queue, i.e., the user is not already using queue resources above his/her limit. The Scheduler also makes sure that there is enough free memory in the TaskTracker to tun the job's task, in case the job has special memory requirements.
Once a job is selected, the Scheduler picks a task to run. This logic to pick a task remains unchanged from earlier versions.
From Apache official documentation on CapacityScheduler:
Resource-based Scheduling - Support for resource-intensive applications, where-in a application can optionally specify higher resource-requirements than the default, there-by accomodating applications with differing resource requirements. Currently, memory is the the resource requirement supported.
From Apache official documentation on FairScheduler:
Fair scheduling is a method of assigning resources to applications such that all apps get, on average, an equal share of resources over time. Hadoop NextGen is capable of scheduling multiple resource types. By default, the Fair Scheduler bases scheduling fairness decisions only on memory. It can be configured to schedule with both memory and CPU, using the notion of Dominant Resource Fairness developed by Ghodsi et al. When there is a single app running, that app uses the entire cluster
Within each queue, a scheduling policy is used to share resources between the running apps. The default is memory-based fair sharing, but FIFO and multi-resource with Dominant Resource Fairness can also be configured. Queues can be arranged in a hierarchy to divide resources and configured with weights to share the cluster in specific proportions.
