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Constructing Dynamic Policies for Paging Mode Selection

Published: 13 August 2018 Publication History

Abstract

Virtualization technology is a key component for data center management which allows for multiple users and applications to share a single, physical machine. Modern virtual machine monitors utilize both software and hardware-assisted paging for memory virtualization, however neither paging mode is always preferable. Previous studies have shown that dynamic selection, which at runtime selects paging modes according to relevant performance metrics, can be effective in tailoring memory virtualization to program workload. However, these approaches require low-level manual analysis, or depend on prior knowledge of workload characteristics and phasing.
We map the problem of dynamic paging mode selection to the contextual bandit, a model for sequential decision making in environments with limited feedback. Utilizing random profiling, which executes a workload while regularly selecting paging modes at random, we construct a paging mode selection policy that dynamically optimizes workload performance given page fault and translation lookaside buffer miss counts. Our approach yields an effective policy, DSP-OFFSET, for the dynamic paging mode selection problem. When trained and evaluated on subsets of the SPEC CPU2006 benchmark suite, DSP-OFFSET achieves speedups up to 44% compared to static paging mode selections, which is equivalent to the performance of the state-of-the-art ASP-SVM model. In addition, DSP-OFFSET requires at most a tenth of the profiling time of ASP-SVM (2.5 hours compared to over 24 hours) to achieve equivalent performance.

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ICPP '18: Proceedings of the 47th International Conference on Parallel Processing
August 2018
945 pages
ISBN:9781450365109
DOI:10.1145/3225058
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Oregon: University of Oregon

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Published: 13 August 2018

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Author Tags

  1. Contextual Bandits
  2. Cost-Sensitive Learning
  3. Memory Management
  4. Support Vector Machines
  5. Virtual Memory Paging

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ICPP 2018

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ICPP '18 Paper Acceptance Rate 91 of 313 submissions, 29%;
Overall Acceptance Rate 91 of 313 submissions, 29%

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