Workload patterns for quality-driven dynamic cloud service configuration and auto-scaling
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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 an iterative 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-based technique that combines a pattern matching 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 based on for example exponential smoothing.
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Zhang, L; Zhang, Y; Jamshidi, P; Xu, L; Pahl, C (Springer Open, 2015)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, ...
Zhang L; Zhang B; Pahl C; Xu L; Zhu Z (Springer, 2013)Recent service management needs, e.g., in the cloud, require ser-vices to be managed dynamically. Services might need to be selected or re-placed at runtime. For services with similar functionality, one approach is to ...
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