Proactive virtualized resource management for service workflows in the cloud

Yi Wei, M. Brian Blake

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.

Original languageEnglish
JournalComputing
DOIs
StateAccepted/In press - Jul 26 2014
Externally publishedYes

Fingerprint

Resource Management
Work Flow
Resources
Resource allocation
Shrinking
Industry
Business Process
Resource Allocation
Termination
Workload
Experiments
Charge
Scaling
Requirements
Experimental Results
Experiment
Simulation

Keywords

  • Adaptive resource management
  • Cloud computing
  • Service workflow
  • Simulation with synthetic data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Software
  • Computer Science Applications

Cite this

Proactive virtualized resource management for service workflows in the cloud. / Wei, Yi; Blake, M. Brian.

In: Computing, 26.07.2014.

Research output: Contribution to journalArticle

@article{f47cfbd20cf4415cbe53fa8375fe306e,
title = "Proactive virtualized resource management for service workflows in the cloud",
abstract = "A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.",
keywords = "Adaptive resource management, Cloud computing, Service workflow, Simulation with synthetic data",
author = "Yi Wei and Blake, {M. Brian}",
year = "2014",
month = "7",
day = "26",
doi = "10.1007/s00607-014-0419-4",
language = "English",
journal = "Computing (Vienna/New York)",
issn = "0010-485X",
publisher = "Springer Wien",

}

TY - JOUR

T1 - Proactive virtualized resource management for service workflows in the cloud

AU - Wei, Yi

AU - Blake, M. Brian

PY - 2014/7/26

Y1 - 2014/7/26

N2 - A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.

AB - A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.

KW - Adaptive resource management

KW - Cloud computing

KW - Service workflow

KW - Simulation with synthetic data

UR - http://www.scopus.com/inward/record.url?scp=84904522711&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904522711&partnerID=8YFLogxK

U2 - 10.1007/s00607-014-0419-4

DO - 10.1007/s00607-014-0419-4

M3 - Article

JO - Computing (Vienna/New York)

JF - Computing (Vienna/New York)

SN - 0010-485X

ER -