论文标题
在工作流程中确定任务复制的强化学习分析
Analysis of Reinforcement Learning for determining task replication in workflows
论文作者
论文摘要
在志愿者计算资源上执行工作流程，其中可能被迫放弃其资源的主要用途资源导致无法预测性，并且通常会大大增加执行时间。任务复制是一种可以改善这一挑战的方法。这是以可能显着增加系统负载和能源消耗的代价。我们建议使用加固学习（RL），以便系统可以“学习”“最佳”复制品，以增加工作流程的数量，这些工作流程迅速完成，同时将复制品无益的情况下最小化系统上的额外工作量。我们通过模拟显示，与固定数量的复制品相比，我们可以使用RL节省34％的能源消耗，而工作流程仅减少了4％，从而实现了预定的开销结合。
Executing workflows on volunteer computing resources where individual tasks may be forced to relinquish their resource for the resource's primary use leads to unpredictability and often significantly increases execution time. Task replication is one approach that can ameliorate this challenge. This comes at the expense of a potentially significant increase in system load and energy consumption. We propose the use of Reinforcement Learning (RL) such that a system may `learn' the `best' number of replicas to run to increase the number of workflows which complete promptly whilst minimising the additional workload on the system when replicas are not beneficial. We show, through simulation, that we can save 34% of the energy consumption using RL compared to a fixed number of replicas with only a 4% decrease in workflows achieving a pre-defined overhead bound.
