Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and\navailability of their lower-level platform infrastructure. While availability management is more mature, performance\nmanagement is less reliable. In order to support a continuous approach that supports the initial static infrastructure\nconfiguration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We\npropose a prediction technique that combines a workload pattern mining approach with a traditional collaborative\nfiltering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common\ninfrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration\nmechanism before more complex traditional methods are considered. This enhances current reactive rule-based\nscalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are\nadditional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log\nsmoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these\nchallenges.
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