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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Adaptive request scheduling for the I/O forwarding layer using reinforcement learning
ترجمه فارسی عنوان مقاله:
زمانبندی درخواست تطبیقی برای لایه انتقال ورودی و خروجی با استفاده از یادگیری تقویتی
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 112 (2020) 1156-1169. doi:10.1016/j.future.2020.05.005
نویسنده:
Jean Luca Bez a,c,∗, Francieli Zanon Boito b, Ramon Nou c, Alberto Miranda c, Toni Cortes c,d, Philippe O.A. Navaux
چکیده انگلیسی:
In this paper, we propose an approach to adapt the I/O forwarding layer of HPC systems to applications’
access patterns. I/O optimization techniques can improve performance for the access patterns they
were designed to target, but they often decrease performance for others. Furthermore, these techniques
usually depend on the precise tune of their parameters, which commonly falls back to the users.
Instead, we propose to do it dynamically at runtime based on the I/O workload observed by the system.
Our approach uses a reinforcement learning technique – contextual bandits – to make the system
capable of learning the best parameter value to each observed access pattern during its execution.
That eliminates the need of a complicated and time-consuming previous training phase. Our case study
is the TWINS scheduling algorithm, where performance improvements depend on the time window
parameter, which in turn depends on the workload. We evaluate our proposal and demonstrate it
can reach a precision of 88% on the parameter selection in the first hundreds of observations of an
access pattern, achieving 99% of the optimal performance. We demonstrate that the system – which
is expected to live for years – will be able to adapt to changes and optimize its performance after
having observed an access pattern for a few (not necessarily contiguous) minutes.
Keywords: High performance I/O | Parallel I/O | I/O scheduling | I/O forwarding | Reinforcement learning | Auto-tuning
قیمت: رایگان
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