Power efficient job scheduling by predicting the impact of processor manufacturing variability

Abstract

Modern CPUs suffer from performance and power consumption variability due to the manufacturing process. As a result, systems that do not consider such variability caused by manufacturing issues lead to performance degradations and wasted power. In order to avoid such negative impact, users and system administrators must actively counteract any manufacturing variability. In this work we show that parallel systems benefit from taking into account the consequences of manufacturing variability when making scheduling decisions at the job scheduler level. We also show that it is possible to predict the impact of this variability on specific applications by using variabilityaware power prediction models. Based on these power models, we propose two job scheduling policies that consider the effects of manufacturing variability for each application and that ensure that power consumption stays under a systemwide power budget. We evaluate our policies under different power budgets and traffic scenarios, consisting of both singleand multi-node parallel applications, utilizing up to 4096 cores in total. We demonstrate that they decrease job turnaround time, compared to contemporary scheduling policies used on production clusters, up to 31% while saving up to 5.5% energy. CCS CONCEPTS • Computer systems organization → Parallel architectures; • Hardware → Power estimation and optimization;

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“…There are a broad group of knowledge bases on the Internet, for example, Kaggle, 5 UCI Repository, 6 Quandl, 7 and MSCOCO. 8…”

“…The scheduling problem, in a general view, comprises both a set of resources and a set of consumers [8]. Its focus is to find an appropriate policy to manage the use of resources by several consumers in order to optimize a particular performance metric chosen as a parameter.…”

“…We define static scheduling considering the scheduling grain as a task [8]. If data such as information about the processors, the execution time of the tasks, the size of the data, the communication pattern, and the dependency relation among the tasks are known in advance, we can affirm that we have a static or deterministic scheduling model.…”

“…https://www.kaggle.com6 https://archive.ics.uci.edu/ml/index.php7 https://www.quandl.com/8 http://cocodataset.org/home7Looking at Data Science through the Lens of Scheduling and Load Balancing DOI: http://dx.doi.org/10.5772/intechopen.92578 …”

An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-Management

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