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paper's Introduction

硕士论文工程

目标

  • 在能耗约束下提升性能,降低成本

  • 在考虑容错(可靠性)的约束下提升性能,降低成本

  • 两者约束前提下提升性能降低成本(未实现)

总体思路

  1. 将任务通过K-Means分类后为任务排序

  2. 遍历任务队列,对当前任务计算当前任务的能耗约束

  3. 遍历每个节点获得Node

  4. 判断Node类型与Task是否一致,一致继续

  5. 计算任务在当前节点执行的能耗大小并添加进队列中

  6. 遍历整个符合能耗约束的节点,选择EFT最小(调度长度)的,即执行时间最小的(降低成本)

  7. 完成所有任务后计算SL(G)与全局能耗以及成本作为实验依据

模型(随机生成模型)

任务排序

  • 分类后的任务排序

  • 其中任务在所有节点的平均执行时间

  • 任务在单节点上的执行时间为

能耗

  • 子任务能耗约束=总能耗约束-前者消耗能耗-后者预分配能耗值

  • 其中前者的能耗

  • 后者的能耗值为预分配最小的能耗,即以最低频率选择能耗最小的节点上执行的能耗值

调度长度

  • 任务调度长度为SL(G)=minEFT(nexit)

  • 实际上AFT

全局任务能耗

  • 计算最终的真是能耗结果

可靠性

  • 子任务可靠性约束

  • 其中预分配的可靠性为

成本

  • 成本模型

paper's People

Contributors

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