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Service workload patterns for QoS-driven cloud resource management

Zhang, Li, Zhang, Yichuan, Jamshidi, Pooyan, Xu, Lei and Pahl, Claus orcid logoORCID: 0000-0002-9049-212X (2015) Service workload patterns for QoS-driven cloud resource management. Journal of Cloud Computing: Advances, Systems and Applications, 4 (23). ISSN 2192-113X

Abstract
Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Quality of Service; Resource Management; Cloud Scalability; Web and Cloud Services; QoS Prediction; Workload Pattern Mining; Uncertainty
Subjects:Computer Science > Software engineering
DCU Faculties and Centres:Research Initiatives and Centres > Irish Centre for Cloud Computing and Commerce (IC4)
Research Initiatives and Centres > Lero: The Irish Software Engineering Research Centre
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Springer Open
Official URL:http://dx.doi.org/10.1186/s13677-015-0048-2
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20933
Deposited On:26 Jan 2016 14:59 by Claus Pahl . Last Modified 20 Jan 2021 14:13
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