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Autonomic deployment decision making for big data analytics applications in the cloud

Lu, Qinghua; Li, Zheng LU ; Zhang, Weishan and Yang, Laurence T. (2017) In Soft Computing 21(16). p.4501-4512
Abstract

When changes happen to big data analytics (BDA) applications in the Cloud at runtime, the affected BDA applications have to be re-deployed to accommodate the changes. Deciding the most suitable deployment is critical and complicated. Although there have been various research studies working on BDA application management, autonomic deployment decision making is still an open research issue. This paper proposes a deployment decision making solution for BDA applications in the Cloud: first, we propose a novel language, named DepPolicy, to specify runtime deployment information as policies; second, we model the deployment decision making problem as a constraint programming problem using MiniZinc; third, we propose a decision making... (More)

When changes happen to big data analytics (BDA) applications in the Cloud at runtime, the affected BDA applications have to be re-deployed to accommodate the changes. Deciding the most suitable deployment is critical and complicated. Although there have been various research studies working on BDA application management, autonomic deployment decision making is still an open research issue. This paper proposes a deployment decision making solution for BDA applications in the Cloud: first, we propose a novel language, named DepPolicy, to specify runtime deployment information as policies; second, we model the deployment decision making problem as a constraint programming problem using MiniZinc; third, we propose a decision making algorithm that can make different deployment decisions for different jobs in a way that maximises overall utility while satisfying all given constraints (e.g., cost limit); fourth, we design and implement a decision making middleware, named DepWare, for BDA application deployment in the Cloud. The proposed solution is evaluated in terms of feasibility, functional correctness, performance and scalability.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Autonomic computing, Big data analytics, Cloud, Decision making, Deployment, QoS
in
Soft Computing
volume
21
issue
16
pages
12 pages
publisher
Elsevier
external identifiers
  • scopus:84947592799
ISSN
1432-7643
DOI
10.1007/s00500-015-1945-5
language
English
LU publication?
yes
id
258df235-bcf2-4df3-b51b-4af184e5ec27
date added to LUP
2018-01-18 10:49:01
date last changed
2018-06-24 05:20:00
@article{258df235-bcf2-4df3-b51b-4af184e5ec27,
  abstract     = {<p>When changes happen to big data analytics (BDA) applications in the Cloud at runtime, the affected BDA applications have to be re-deployed to accommodate the changes. Deciding the most suitable deployment is critical and complicated. Although there have been various research studies working on BDA application management, autonomic deployment decision making is still an open research issue. This paper proposes a deployment decision making solution for BDA applications in the Cloud: first, we propose a novel language, named DepPolicy, to specify runtime deployment information as policies; second, we model the deployment decision making problem as a constraint programming problem using MiniZinc; third, we propose a decision making algorithm that can make different deployment decisions for different jobs in a way that maximises overall utility while satisfying all given constraints (e.g., cost limit); fourth, we design and implement a decision making middleware, named DepWare, for BDA application deployment in the Cloud. The proposed solution is evaluated in terms of feasibility, functional correctness, performance and scalability.</p>},
  author       = {Lu, Qinghua and Li, Zheng and Zhang, Weishan and Yang, Laurence T.},
  issn         = {1432-7643},
  keyword      = {Autonomic computing,Big data analytics,Cloud,Decision making,Deployment,QoS},
  language     = {eng},
  month        = {08},
  number       = {16},
  pages        = {4501--4512},
  publisher    = {Elsevier},
  series       = {Soft Computing},
  title        = {Autonomic deployment decision making for big data analytics applications in the cloud},
  url          = {http://dx.doi.org/10.1007/s00500-015-1945-5},
  volume       = {21},
  year         = {2017},
}