Towards a Multi-model Cloud Workflow Resource Monitoring, Adaptation, and Prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Workflow configuration, re-configuration execution, monitoring and adaptation over a cloud environment are considered very challenging activities. This is due to the fact that such activities are resource-aware, require intensive processing, and should adapt to dynamic cloud changes. In this research, we propose a multi-model for workflow resource monitoring, resource prediction, and resource adaptations. Three adaptation strategies are proposed to capture changes in environment resources, categorize various violations and take the necessary actions to adapt resources according to workflow needs. Workflow resource prediction uses ARIMA to predict resource shortage and support adequate adaptation. However, extreme adaptation is supported by continuously monitoring various workflow environment entities. We also evaluate workflow trust based on QoS to support the different adaptations strategies. We implemented our model on a cloud environment and we experimented different adaptation scenarios. The results validated the effectiveness of our monitoring, prediction and adaptation schemes in detecting violations and hence, predicting accurately cloud resource shortages and takes the appropriate actions to deal with these violations.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1755-1762
Number of pages8
ISBN (Print)9781538643877
DOIs
Publication statusPublished - Sep 5 2018
Event17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 - New York, United States
Duration: Jul 31 2018Aug 3 2018

Publication series

NameProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

Other

Other17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Country/TerritoryUnited States
CityNew York
Period7/31/188/3/18

Keywords

  • adaptation
  • cloud
  • prediction
  • trust assessment
  • workflow

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Fingerprint

Dive into the research topics of 'Towards a Multi-model Cloud Workflow Resource Monitoring, Adaptation, and Prediction'. Together they form a unique fingerprint.

Cite this