IaaS Signature Change Detection with Performance Noise

We propose a novel framework to detect changes in the performance behavior of an IaaS service. The proposed framework leverages the concept of the IaaS signature to represent an IaaS service’s longterm performance behavior. A new type of performance signature called categorical IaaS signature is introduced to represent the performance behavior more accurately. A novel performance noise model is proposed to accurately identify IaaS performance noise and accurate changes in the performance behavior of an IaaS service. A set of experiments based on real-world datasets is carried out to evaluate the effectiveness of the proposed framework.

[1]  Evgenia Smirni,et al.  Analysis of application performance and its change via representative application signatures , 2008, NOMS 2008 - 2008 IEEE Network Operations and Management Symposium.

[2]  Zibin Zheng,et al.  A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation , 2015, 2015 IEEE International Conference on Web Services.

[3]  A. Zénon,et al.  Learning and forgetting using reinforced Bayesian change detection , 2019, PLoS computational biology.

[4]  Jinjun Chen,et al.  A History Record-Based Service Optimization Method for QoS-Aware Service Composition , 2011, 2011 IEEE International Conference on Web Services.

[5]  Philipp Leitner,et al.  Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds , 2014, ACM Trans. Internet Techn..

[6]  Athman Bouguettaya,et al.  Fine-grained Conflict Detection of IoT Services , 2020, 2020 IEEE International Conference on Services Computing (SCC).

[7]  Diane J. Cook,et al.  A survey of methods for time series change point detection , 2017, Knowledge and Information Systems.

[8]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[9]  E. S. Page Cumulative Sum Charts , 1961 .

[10]  K. Stout Cumulative Sum Charts , 1985 .

[11]  Benjamin Farley,et al.  Resource-freeing attacks: improve your cloud performance (at your neighbor's expense) , 2012, CCS.

[12]  Wei Wang,et al.  Testing Cloud Applications under Cloud-Uncertainty Performance Effects , 2018, 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST).

[13]  Alexandru Iosup,et al.  On the Performance Variability of Production Cloud Services , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[14]  Athman Bouguettaya,et al.  Signature-based Selection of IaaS Cloud Services , 2020, 2020 IEEE International Conference on Web Services (ICWS).

[15]  Athman Bouguettaya,et al.  Event-based Detection of Changes in IaaS Performance Signatures , 2020, 2020 IEEE International Conference on Services Computing (SCC).

[16]  Alexandru Iosup,et al.  IaaS cloud benchmarking: approaches, challenges, and experience , 2013, HotTopiCS '13.

[17]  Dror G. Feitelson,et al.  Workload Modeling for Performance Evaluation , 2002, Performance.

[18]  Anneke Zuiderwijk,et al.  Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector , 2012, dg.o '12.