Energy-efficient resource allocation and provisioning for in-memory database clusters
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Accepted version
Author(s)
Karsten, M
Casale, G
Type
Conference Paper
Abstract
Systems for processing large scale analytical work-
loads are increasingly moving from on-premise setups to on-
demand configurations deployed on scalable cloud infrastruc-
tures. To reduce the cost of such infrastructures, existing research
focuses on developing novel methods for workload and server
consolidation. In this paper, we combine analytical modeling
and non-linear optimization to help cloud providers increase
the energy-efficiency of in-memory database clusters in cloud
environments. We model this scenario as a multi-dimensional bin-
packing problem and propose a new approach based on a hybrid
genetic algorithm that efficiently handles resource allocation and
server assignment for a given set of in-memory databases. Our
trace-driven evaluation is based on measurements from an SAP
HANA in-memory system and indicates improvements between
6% and 32% over the popular best-fit decreasing heuristic.
loads are increasingly moving from on-premise setups to on-
demand configurations deployed on scalable cloud infrastruc-
tures. To reduce the cost of such infrastructures, existing research
focuses on developing novel methods for workload and server
consolidation. In this paper, we combine analytical modeling
and non-linear optimization to help cloud providers increase
the energy-efficiency of in-memory database clusters in cloud
environments. We model this scenario as a multi-dimensional bin-
packing problem and propose a new approach based on a hybrid
genetic algorithm that efficiently handles resource allocation and
server assignment for a given set of in-memory databases. Our
trace-driven evaluation is based on measurements from an SAP
HANA in-memory system and indicates improvements between
6% and 32% over the popular best-fit decreasing heuristic.
Date Issued
2017-07-24
Date Acceptance
2016-11-11
Citation
2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2017
Publisher
IEEE
Journal / Book Title
2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Grant Number
644869
Source
IEEE/IFIP IM International Symposium on Integrated Network Management
Publication Status
Published
Start Date
2017-05-08
Finish Date
2017-05-12
Coverage Spatial
Lisbon