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#: ../../source/development/services/scheduling.rst:4
msgid "Scheduling Service"
msgstr "Scheduling 服务"

#: ../../source/development/services/scheduling.rst:9
msgid "Scheduling Policy"
msgstr "调度策略"

#: ../../source/development/services/scheduling.rst:10
msgid ""
"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."
msgstr ""
"当一个 Operand 组成的 Graph 执行时，合适的执行顺序会减少集群中暂存的数据"
"总量，从而减小数据被 Spill 到磁盘的可能性。合适的 Worker 能够减少执行时"
"网络传输的总量。"

#: ../../source/development/services/scheduling.rst:16
msgid "Operand Selection"
msgstr "Operand 选择策略"

#: ../../source/development/services/scheduling.rst:17
msgid ""
"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."
msgstr ""
"合适的执行顺序能够显著减小集群中暂存的数据总量。下图中展示了 Tree "
"Reduction 的例子，圆形代表 Operand，方形代表 Chunk，红色代表 Operand 正在"
"执行，蓝色代表 Operand 可被执行，绿色代表 Operand 产生的 Chunk 已被存储，"
"灰色代表 Operand 及其相关数据已被释放。假设我们有两台 Worker，并且每个 "
"Operand 的资源使用量均相等，每张图展示的是不同策略下经过5个时间单元的执行"
"后的状态。左图展示的是节点依照层次分别执行，而右图展示的是依照接近深度"
"优先的顺序执行。左图中，有6个 Chunk 的数据需要暂存，右图只有2个。"

#: ../../source/development/services/scheduling.rst:31
msgid ""
"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:"
msgstr ""
"因为我们的目标是减少存储在集群中的数据总数，我们为进入 ``READY`` 状态的 "
"Operand 设定了一套优先级策略："

#: ../../source/development/services/scheduling.rst:35
msgid "The operand with greater depth shall be executed earlier;"
msgstr "深度更大的 Operand 需要被优先执行；"

#: ../../source/development/services/scheduling.rst:36
msgid "The operand required by deeper operands shall be executed earlier;"
msgstr "被更深的 Operand 依赖的 Operand 需要被优先执行；"

#: ../../source/development/services/scheduling.rst:37
msgid "The operand with smaller output size shall be executed first."
msgstr "输出规模更小的节点需要被优先执行。"

#: ../../source/development/services/scheduling.rst:40
msgid "Worker Selection"
msgstr "Worker 选择策略"

#: ../../source/development/services/scheduling.rst:41
msgid ""
"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."
msgstr ""
"当 Supervisor 准备执行图时，初始 Operand 的 Worker 已被确定。我们选择后续"
" Operand 分配 Worker 的依据是输入数据所在的 Worker。如果某个 Worker 拥有"
"的输入数据大小最大，则该 Worker 将被选择用于执行后续 Operand。如果这样的 "
"Worker 有多个，则各个候选 Worker 的资源状况将起到决定作用。"

#: ../../source/development/services/scheduling.rst:47
msgid "Configuration"
msgstr "配置"

#: ../../source/development/services/scheduling.rst:58
msgid "APIs"
msgstr "接口"

#: ../../source/development/services/scheduling.rst:65:<autosummary>:1
msgid ""
":obj:`SchedulingAPI <mars.services.scheduling.SchedulingAPI>`\\ "
"\\(session\\_id\\, address\\[\\, ...\\]\\)"
msgstr ""

