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Note that our algorithm shows a substantial improvement with respect to both AVERAGE RATE heuristic and OPTIMAL AVAILABLE heuristic, while maintaining a low competitive ratio even when the prediction error is high

Learning Augmented Energy Minimization via Speed Scaling

NIPS 2020, (2020)

被引用5|浏览27
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摘要

As power management has become a primary concern in modern data centers, computing resources are being scaled dynamically to minimize energy consumption. We initiate the study of a variant of the classic online speed scaling problem, in which machine learning predictions about the future can be integrated naturally. Inspired by recent w...更多
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简介
  • Online problems can be informally defined as problems where the authors are required to make irrevocable decisions without knowing the future.
  • In which the jobs are revealed only at their release time, Yao et al designed two different algorithms: (1) the AVERAGE RATE heuristic (AVR), for which they proved a bound of 2α−1αα on the competitive ratio.
  • The authors prove that no online algorithm can have a competitive ratio better than Ω((6/5)α) even in the uniform case.
重点内容
  • Online problems can be informally defined as problems where we are required to make irrevocable decisions without knowing the future
  • Due to the success story of machine learning (ML), a recent line of work, first proposed by Lykouris and Vassilvitskii [13] and Medina and Vassilvitskii [14], suggests incorporating the predictions provided by ML algorithms in the design of online algorithms
  • An obvious caveat is that ML predictors often come with no worst-case guarantees and so we would like our algorithm to be robust to misleading predictions
  • We focus on our main contribution which is the design and analysis of a simple and efficient algorithm which incorporates any ML predictor as a black-box without making any further assumption
  • Note that our algorithm shows a substantial improvement with respect to both AVERAGE RATE heuristic (AVR) and OPTIMAL AVAILABLE heuristic (OA), while maintaining a low competitive ratio even when the prediction error is high
  • While our work considers a specific problem related to scheduling, we would like to emphasize that a considerable percentage of real-world systems already have the ability to dynamically scale their computing resources2 to minimize their energy consumption
结果
  • In the following the authors denote by OPT the energy cost of the optimal offline schedule and by ε > 0 a robustness parameter of the algorithm, the smaller ε is the more the authors trust the prediction.
  • It is easy to see that the algorithm is consistent: If the prediction of wireal is perfect, the job will be scheduled at speed ci in the interval [ai, bi].
  • For every 0 < δ 1, the cost of the schedule produced by the algorithm LAS-TRUST is bounded by (1 + δ)α OPT +(12/δ)α · err .
  • The authors will first relate the energy of the schedule s(t) to the optimal energy for the predicted instance, i.e., OPT(wpred).
  • The authors describe a method ROBUSTIFY that takes any online algorithm which guarantees to complete each job in (1 − δ)D time, that is, with some slack to its deadline, and turns it into a robust algorithm without increasing the energy of the schedule produced.
  • Input: T , D, and wpred initially and wreal in an online fashion OLeuttδpu>t:0Awfiethasib11l−+eδδscαhe=du1le+(sεi.)Ti=−0D Compute optimal offline schedule for (wpred, T, (1 − δ)D) where the jobs wipred are run at uniform speeds ci an disjoint intervals [ai, bi] using [17].
  • The authors note that in the first case, where the predictor is relatively accurate but still noisy, LAS is consistently better than any online algorithm achieving a competitive ratio close to 1 for small values of ε.
结论
  • The predictor tries to mislead the algorithm by creating a prediction which constitutes a symmetric (around (m + M )/2) random walk with respect to the true instance.
  • Note that the algorithm shows a substantial improvement with respect to both AVR and OA, while maintaining a low competitive ratio even when the prediction error is high.
  • While the work considers a specific problem related to scheduling, the authors would like to emphasize that a considerable percentage of real-world systems already have the ability to dynamically scale their computing resources2 to minimize their energy consumption.
表格
  • Table1: Artificial dataset results
  • Table2: Real dataset results with different α values
Download tables as Excel
相关工作
  • On the one hand, the field of learning augmented algorithms is relatively new, with a lot of recent exciting results (see for example Gollapudi and Panigrahi [7], Hsu et al [8], Kodialam [10], Lattanzi et al [11], Lee et al [12], Lykouris and Vassilvitskii [13], Medina and Vassilvitskii [14], Purohit et al [15], Xu and Xu [16]). On the other hand, the speed scaling problem proposed by Yao et al in [17] is well understood in both the offline and online setting. In its full generality, a set of tasks each with different arrival times, deadlines, and workloads needs to be completed in time while the speed is scaled in order to minimize energy. In the offline setting Yao et al proved that the problem can be solved in polynomial time by a greedy algorithm. In the online setting, in which the jobs are revealed only at their release time, Yao et al designed two different algorithms: (1) the AVERAGE RATE heuristic (AVR), for which they proved a bound of 2α−1αα on the competitive ratio. This analysis was later proved to be asymptotically tight by Bansal et al [3]. (2) The OPTIMAL AVAILABLE heuristic (OA), which was shown to be αα-competitive in [2]. In the same paper, Bansal et al proposed a third online algorithm named BKP for which they proved a competitive ratio asymptotically equivalent to eα. While these competitive ratios exponential in α might not seem satisfying, Bansal et al also proved that the exponential dependency cannot be better than eα. A number of variants of the problem have also been considered in the offline setting (no preemption allowed, precedence constraints, nested jobs and more listed in a recent survey by Gerards et al [6]) and under a stochastic optimization point of view (see for instance [1]). It is important to note that, while in theory the problem is interesting in the general case i.e. when α is an input parameter, in practice we usually focus on small values of α such as 2 or 3 since they model certain physical laws (see e.g. Bansal et al [2]). Although the BKP algorithm provides the best asymptotic guarantee, OA or AVR often lead to better solutions for small α and therefore remain relevant.
基金
  • Acknowledgments and Disclosure of Funding This research is supported by the Swiss National Science Foundation project 200021-184656 “Randomness in Problem Instances and Randomized Algorithms”
  • Andreas Maggiori was supported by the Swiss National Science Fund (SNSF) grant no 200020_182517/1 “Spatial Coupling of Graphical Models in Communications, Signal Processing, Computer Science and Statistical Physics”
引用论文
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  • Nikhil Bansal, Tracy Kimbrel, and Kirk Pruhs. Speed scaling to manage energy and temperature. J. ACM, 54(1):3:1–3:39, 2007. doi: 10.1145/1206035.1206038. URL https://doi.org/10.1145/1206035.1206038.
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  • Nikhil Bansal, David P. Bunde, Ho-Leung Chan, and Kirk Pruhs. Average rate speed scaling. In LATIN 2008: Theoretical Informatics, 8th Latin American Symposium, Búzios, Brazil, April 7-11, 2008, Proceedings, pages 240–251, 2008. doi: 10.1007/978-3-540-78773-0\_21. URL https://doi.org/10.1007/978-3-540-78773-0_21.
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    Findings
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  • Sreenivas Gollapudi and Debmalya Panigrahi. Online algorithms for rent-or-buy with expert advice. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, pages 2319–2327, 2019. URL http://proceedings.mlr.press/v97/gollapudi19a.html.
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  • Chen-Yu Hsu, Piotr Indyk, Dina Katabi, and Ali Vakilian. Learning-based frequency estimation algorithms. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019. URL https://openreview.net/forum?id=r1lohoCqY7.
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    Findings
  • 1. If the algorithm processes more that γ units of work on job 1 before time 1 then for instance J1 the energy cost is at least γα. Hence the competitive ratio is at least γα · 2α−1.
    Google ScholarFindings
  • 2. On the contrary, if the algorithm works less than γ units of work before the release of the second job then in instance J2 the algorithm has to complete at least 3 − γ units of work between time 1 and 3.
    Google ScholarFindings
  • 1. If the algorithm works more that 1/2 then the energy spent by the algorithm until time εD
    Google ScholarFindings
  • 2. However, if it works less than 1/2 then on instance J2, a total work of at least (1/ε + 1 − 1/2) = (1/2 + 1/ε) remains to be done in D time units. Hence the energy consumption on instance J2 is at least
    Google ScholarFindings
  • 1. For any k 0, the machine is never idle in interval Ik.
    Google ScholarFindings
  • 2. For any k 0, all jobs that are processed in Ik have a deadline dj.
    Google ScholarFindings
作者
Etienne Bamas
Etienne Bamas
Andreas Maggiori
Andreas Maggiori
Lars Rohwedder
Lars Rohwedder
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