Autonomic deployment decision making for big data analytics applications in the cloud
Title
Autonomic deployment decision making for big data analytics applications in the cloud
Subject
Decision making
Big data
Constraint theory
Middleware
Cloud analytics
Constraint satisfaction problems
Description
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. 2015, Springer-Verlag Berlin Heidelberg.
4501-4512
16
21
Creator
Lu, Qinghua
Li, Zheng
Zhang, Weishan
Yang, Laurence T.
Publisher
Soft Computing
Date
2017
Type
journalArticle
Identifier
14327643
10.1007/s00500-015-1945-5
URL
http://dx.doi.org/10.1007/s00500-015-1945-5
Citation
Lu, Qinghua et al., “Autonomic deployment decision making for big data analytics applications in the cloud,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/29033.