Service workload patterns for QoS-driven cloud resource management
Zhang, Li, Zhang, Yichuan, Jamshidi, Pooyan, Xu, Lei and Pahl, ClausORCID: 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
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