TY - GEN
T1 - Towards a Multi-model Cloud Workflow Resource Monitoring, Adaptation, and Prediction
AU - Serhani, Mohamed Adel
AU - El Kassabi, Hadee T.
AU - Al Qirim, Nabeel
AU - Navaz, Alramzana N.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/5
Y1 - 2018/9/5
N2 - 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.
AB - 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.
KW - adaptation
KW - cloud
KW - prediction
KW - trust assessment
KW - workflow
UR - http://www.scopus.com/inward/record.url?scp=85054062976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054062976&partnerID=8YFLogxK
U2 - 10.1109/TrustCom/BigDataSE.2018.00265
DO - 10.1109/TrustCom/BigDataSE.2018.00265
M3 - Conference contribution
AN - SCOPUS:85054062976
SN - 9781538643877
T3 - Proceedings - 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
SP - 1755
EP - 1762
BT - Proceedings - 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
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 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
Y2 - 31 July 2018 through 3 August 2018
ER -