Service workload patterns for QoS-driven cloud resource management
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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 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.
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Zhang L; Zhang Y; Jamshidi P; Xu L; Pahl C (IEEE, 2014)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 ...
A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans Li H; Gan W; Lu L; Dong X; Han X; Hu C; Yang Z; Sun L; Bao W; Li P; He M; Wang Y; Zhu J; Ning Q; Tang Y; Zhang R; Wen J; Wang D; Zhu X; Guo K; Zuo X; Guo X; Yang H; Zhou X; DIAGRAM Consortium; AGEN-T2D Consortium; Zhang X; Qi L; Loos RJ; Hu FB; Wu T; Liu Y; Liu L; Hu R; Jia W; Ji L; Li Y; Lin X (2013)Substantial progress has been made in identification of type 2 diabetes (T2D) risk loci in the past few years, but our understanding of the genetic basis of T2D in ethnically diverse populations remains limited. We performed ...