Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems

McGough, Andrew Stephen; Moubayed, Noura Al; Forshaw, Matthew

doi:10.1145/3053600.3053612

Cited by 4 publications

(2 citation statements)

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“…It should be noted that we do not consider predicting the duration of tasks -although it is possible to train a machine learning algorithm accurately on a set of tasks [22], it is not easy to predict future tasks durations. As although there is significant local correlation between tasks, this correlation dissipates quickly -e.g.…”

Section: Prediction Of Computer Idle Time Through Machine Learningmentioning

confidence: 99%

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

McGough

Forshaw

Brennan

et al. 2018

2018 Ninth International Green and Sustainable Computing Conference (IGSC)

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High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time -volunteer computing. This has great advantages as it gives access to vast quantities of computational power for little or no cost. The downside is that running tasks are sacrificed if the computer is needed for its primary use. Normally terminating the task which must be restarted on a different computer -leading to wasted energy and an increase in task completion time. We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning. By combining two machine learning approaches, namely Random Forest and MultiLayer Perceptron, we save 51.4% of the energy without significantly affecting the time to complete tasks.

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Section: Prediction Of Computer Idle Time Through Machine Learningmentioning

confidence: 99%

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments

McGough

Forshaw

Brennan

et al. 2018

2018 Ninth International Green and Sustainable Computing Conference (IGSC)

Self Cite

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“…The idea is to learn a "meta baseline" that can be specialized to a baseline for a specific input instantiation using a small number of training episodes with that input. This approach applies to applications in which an input sequence can be repeated during training, e.g., applications that use simulations or experiments with previously-collected input traces for training (McGough et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Variance Reduction for Reinforcement Learning in Input-Driven Environments

Mao,

Venkatakrishnan,

Schwarzkopf

et al. 2018

Preprint

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We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

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