EOS: Automatic In-vivo Evolution of Kernel Policies for Better Performance

08/26/2015
by   Yan Cui, et al.
0

Today's monolithic kernels often implement a small, fixed set of policies such as disk I/O scheduling policies, while exposing many parameters to let users select a policy or adjust the specific setting of the policy. Ideally, the parameters exposed should be flexible enough for users to tune for good performance, but in practice, users lack domain knowledge of the parameters and are often stuck with bad, default parameter settings. We present EOS, a system that bridges the knowledge gap between kernel developers and users by automatically evolving the policies and parameters in vivo on users' real, production workloads. It provides a simple policy specification API for kernel developers to programmatically describe how the policies and parameters should be tuned, a policy cache to make in-vivo tuning easy and fast by memorizing good parameter settings for past workloads, and a hierarchical search engine to effectively search the parameter space. Evaluation of EOS on four main Linux subsystems shows that it is easy to use and effectively improves each subsystem's performance.

READ FULL TEXT

Authors

page 8

page 9

page 10

page 11

08/18/2019

Demystifying Learning Rate Polices for High Accuracy Training of Deep Neural Networks

Learning Rate (LR) is an important hyper-parameter to tune for effective...
12/15/2020

Policy Manifold Search for Improving Diversity-based Neuroevolution

Diversity-based approaches have recently gained popularity as an alterna...
11/30/2016

Memory Controller Design Under Cloud Workloads

This work studies the behavior of state-of-the-art memory controller des...
06/29/2021

Restricted Adaptivity in Stochastic Scheduling

We consider the stochastic scheduling problem of minimizing the expected...
02/26/2019

Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies

We consider a settings of hierarchical reinforcement learning, in which ...
04/27/2021

Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution

Neuroevolution is an alternative to gradient-based optimisation that has...
09/14/2020

Disease control as an optimization problem

Traditionally, expert epidemiologists devise policies for disease contro...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.