Parametric optimization of storage systems

E. Zadok; A. Arora; Z. Cao; A. Chaganti; A. Chaudhary; S. Mandal

Parametric optimization of storage systems

E. Zadok
, A. Arora
, Z. Cao
, A. Chaganti
, A. Chaudhary
, S. Mandal

Computer Science

Stony Brook University

Stony Brook University

Research output: Contribution to conference › Paper › peer-review

5 Scopus citations

Abstract

Most storage systems come with large set of parameters to directly or indirectly control a specific set of metrics that may include performance, energy, etc. Often, storage systems are deployed with default configurations, rendering them sub-optimal. Finding optimal configurations is difficult due to the numerous combinations of parameters and parameter sensitivity to workloads and deployed environments. Previous research on parameter optimization was either limited to narrow problems or not widely applicable to storage stack parameter optimization in general. Based on promising early results, we propose using meta-heuristic techniques such as genetic algorithms to efficiently find near-optimal configurations for storage systems.

Original language	English
State	Published - 2015
Event	7th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015 co-located with USENIX ATC 2015 - Santa Clara, United States Duration: Jul 6 2015 → Jul 7 2015

Conference

Conference	7th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015 co-located with USENIX ATC 2015
Country/Territory	United States
City	Santa Clara
Period	07/6/15 → 07/7/15

Cite this

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title = "Parametric optimization of storage systems",

abstract = "Most storage systems come with large set of parameters to directly or indirectly control a specific set of metrics that may include performance, energy, etc. Often, storage systems are deployed with default configurations, rendering them sub-optimal. Finding optimal configurations is difficult due to the numerous combinations of parameters and parameter sensitivity to workloads and deployed environments. Previous research on parameter optimization was either limited to narrow problems or not widely applicable to storage stack parameter optimization in general. Based on promising early results, we propose using meta-heuristic techniques such as genetic algorithms to efficiently find near-optimal configurations for storage systems.",

author = "E. Zadok and A. Arora and Z. Cao and A. Chaganti and A. Chaudhary and S. Mandal",

note = "Publisher Copyright: {\textcopyright} USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015.All right reserved.; 7th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015 co-located with USENIX ATC 2015 ; Conference date: 06-07-2015 Through 07-07-2015",

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TY - CONF

T1 - Parametric optimization of storage systems

AU - Zadok, E.

AU - Arora, A.

AU - Cao, Z.

AU - Chaganti, A.

AU - Chaudhary, A.

AU - Mandal, S.

PY - 2015

Y1 - 2015

N2 - Most storage systems come with large set of parameters to directly or indirectly control a specific set of metrics that may include performance, energy, etc. Often, storage systems are deployed with default configurations, rendering them sub-optimal. Finding optimal configurations is difficult due to the numerous combinations of parameters and parameter sensitivity to workloads and deployed environments. Previous research on parameter optimization was either limited to narrow problems or not widely applicable to storage stack parameter optimization in general. Based on promising early results, we propose using meta-heuristic techniques such as genetic algorithms to efficiently find near-optimal configurations for storage systems.

AB - Most storage systems come with large set of parameters to directly or indirectly control a specific set of metrics that may include performance, energy, etc. Often, storage systems are deployed with default configurations, rendering them sub-optimal. Finding optimal configurations is difficult due to the numerous combinations of parameters and parameter sensitivity to workloads and deployed environments. Previous research on parameter optimization was either limited to narrow problems or not widely applicable to storage stack parameter optimization in general. Based on promising early results, we propose using meta-heuristic techniques such as genetic algorithms to efficiently find near-optimal configurations for storage systems.

UR - https://www.scopus.com/pages/publications/85088227871

M3 - Paper

T2 - 7th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015 co-located with USENIX ATC 2015

Y2 - 6 July 2015 through 7 July 2015

ER -

Parametric optimization of storage systems

Abstract

Conference

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