Advanced search
2 files | 4.84 MB Add to list

BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows

Ankita Atrey (UGent) , Gregory Van Seghbroeck (UGent) , Bruno Volckaert (UGent) and Filip De Turck (UGent)
Author
Organization
Abstract
Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms.
Keywords
CLOUD COMPUTING ENVIRONMENTS, A-SERVICE APPLICATIONS, Cloud resource provisioning, workflows, cloud scalability, adaptive, clustering, knowledge base, deadline-constraints, SLA, cloud simulation

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 2.31 MB
  • 7207 i.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 2.53 MB

Citation

Please use this url to cite or link to this publication:

MLA
Atrey, Ankita, Gregory Van Seghbroeck, Bruno Volckaert, et al. “BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows.” IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 15.3 (2018): 894–908. Print.
APA
Atrey, A., Van Seghbroeck, G., Volckaert, B., & De Turck, F. (2018). BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 15(3), 894–908.
Chicago author-date
Atrey, Ankita, Gregory Van Seghbroeck, Bruno Volckaert, and Filip De Turck. 2018. “BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows.” Ieee Transactions on Network and Service Management 15 (3): 894–908.
Chicago author-date (all authors)
Atrey, Ankita, Gregory Van Seghbroeck, Bruno Volckaert, and Filip De Turck. 2018. “BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows.” Ieee Transactions on Network and Service Management 15 (3): 894–908.
Vancouver
1.
Atrey A, Van Seghbroeck G, Volckaert B, De Turck F. BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT. Piscataway: Ieee-inst Electrical Electronics Engineers Inc; 2018;15(3):894–908.
IEEE
[1]
A. Atrey, G. Van Seghbroeck, B. Volckaert, and F. De Turck, “BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows,” IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, vol. 15, no. 3, pp. 894–908, 2018.
@article{8575343,
  abstract     = {Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms.},
  author       = {Atrey, Ankita and Van Seghbroeck, Gregory and Volckaert, Bruno and De Turck, Filip},
  issn         = {1932-4537},
  journal      = {IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT},
  keywords     = {CLOUD COMPUTING ENVIRONMENTS,A-SERVICE APPLICATIONS,Cloud resource provisioning,workflows,cloud scalability,adaptive,clustering,knowledge base,deadline-constraints,SLA,cloud simulation},
  language     = {eng},
  number       = {3},
  pages        = {894--908},
  publisher    = {Ieee-inst Electrical Electronics Engineers Inc},
  title        = {BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows},
  url          = {http://dx.doi.org/10.1109/TNSM.2018.2830311},
  volume       = {15},
  year         = {2018},
}

Altmetric
View in Altmetric
Web of Science
Times cited: