Runtime Energy Minimization of Distributed Many-Core Systems using Transfer Learning
Runtime Energy Minimization of Distributed Many-Core Systems using Transfer Learning
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DOI：
10.23919/date54114.2022.9774650
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Dainius Jenkus;F. Xia;R. Shafik;Alexandre Yakovlev
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文献类型：
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作者：
Dainius Jenkus;F. Xia;R. Shafik;Alexandre Yakovlev
计算资源的异构性继续渗透到多核心系统中，这使得能效成为一个具有挑战性的目标。随着系统复杂性增加到分布式系统领域，现有的基于规则和模型驱动的方法返回次优的能量效率和有限的可扩展性。工作负载和服务质量(Qos)需求的动态变化进一步加剧了这种情况。本文提出了一种基于传输学习(TL)驱动的探索策略实现能量最小化的服务质量感知的运行时管理方法。它增强了标准Q学习，以提高学习速度和操作最优性(即，服务质量和能量)。我们方法的核心是跨任务的状态-动作空间的多维知识转移。它加速了动态电压/频率调节(DVFS)控制操作的学习，以实现功率/性能的权衡。首先，该方法识别并在探索的和行为相似的状态之间传输已经学习的策略，称为任务内学习迁移(ITLT)。其次，如果没有类似的“专家”状态可用，它会通过所谓的“州内学习转移”(ISLT)来加速地方州层面的探索。对该方法的比较评估表明，该方法的探索速度更快、更平衡。与现有勘探策略相比，节能7.30%到18.06%，服务质量从10.43%提高到14.3%，体现了这一点。在服务器集群上的WordPress和TensorFlow工作负载下演示了这种方法。
The heterogeneity of computing resources continues to permeate into many-core systems making energy-efficiency a challenging objective. Existing rule-based and model-driven methods return sub-optimal energy-efficiency and limited scalability as system complexity increases to the domain of distributed systems. This is exacerbated further by dynamic variations of workloads and quality-of-service (QoS) demands. This work presents a QoS-aware runtime management method for energy minimization using a transfer learning (TL) driven exploration strategy. It enhances standard Q-learning to improve both learning speed and operational optimality (i.e., QoS and energy). The core to our approach is a multi-dimensional knowledge transfer across a task's state-action space. It accelerates the learning of dynamic voltage/frequency scaling (DVFS) control actions for tuning power/performance trade-offs. Firstly, the method identifies and transfers already learned policies between explored and behaviorally similar states referred to as Intra-Task Learning Transfer (ITLT). Secondly, if no similar “expert” states are available, it accelerates exploration at a local state's level through what's known as Intra-State Learning Transfer (ISLT). A comparative evaluation of the approach indicates faster and more balanced exploration. This is shown through energy savings ranging from 7.30% to 18.06%, and improved QoS from 10.43% to 14.3%, when compared to existing exploration strategies. This method is demonstrated under WordPress and TensorFlow workloads on a server cluster.