Scheduling Service#

Scheduling Policy#

When an operand graph is being executed, proper selection of execution order will reduce total amount of data stored in the cluster, thus reducing the probability that chunks are spilled into disks. Proper selection of workers can also reduce the amount of data needed to transfer in execution.

Operand Selection#

Proper execution order can significantly reduce the number of objects stored in the cluster. We show the example of tree reduction in the graph below, where ovals represent operands and rectangles represent chunks. Red color means that the operand is being executed, and blue color means that the operand is ready for execution. Green color means that the chunk is stored, while the gray color means that chunks or operands are freed. Assume that we have 2 workers, and work load of all operands are the same. Both graphs show one operand selection strategy that is executed after 5 time unit. The left graph show the scenario when nodes are executed in hierarchical order, while the right show that the graph is executed in depth-first order. The strategy on the left graph leaves 6 chunks stored in the cluster, while the right only 2.


Given that our goal is to reduce the amount of data stored in the cluster during execution, we put a priority for operands when they are ready for execution:

  1. The operand with greater depth shall be executed earlier;

  2. The operand required by deeper operands shall be executed earlier;

  3. The operand with smaller output size shall be executed first.

Worker Selection#

The worker of initial operands are decided when the supervisor prepares an operand graph. We choose the worker of descendant operands given the location of input chunks. When there are multiple workers providing minimal network transfer, a worker satisfying resource requirements are selected.


    mem_quota_size: "80%",
    mem_hard_limit: "95%",
    enable_kill_slot: true,
    subtask_max_retries": 1


SchedulingAPI(session_id, address[, ...])