Performance Management of Enterprise Application Systems in the Cloud Era

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Enterprise applications, e.g., Web and interactive big data services, need to respond quickly to user transactions. Consequently, system operators need techniques that ensure applications meet their response time objectives while utilizing computing resources in a cost-effective way. Several factors necessitate new performance management techniques for such systems. For example, these applications are being increasingly deployed on public cloud platforms, which can suffer from unpredictable performance degradations due to contention for shared cloud resources. Novel approaches are needed to manage system performance in the presence of such platform induced interference. Furthermore, these systems typically experience bursty workloads, which can degrade performance in complex ways. This motivates new techniques that can predict and mitigate the impact of burstiness. This program seeks to address such challenges. We will investigate new techniques that allow an operator to accurately predict the cloud resources needed by a system to satisfy a desired response time target while handling a given workload. Techniques based on queuing analysis typically require an expert to manually author a system model. Also, accuracy can be impacted when predicting for bursty workloads. Machine learning (ML) techniques promise a data-driven alternative to queuing analysis. However, existing work does not provide clear intuition on tasks that can have a big impact on accuracy such as ML technique selection, featurization, and training data selection. My program will address this knowledge gap and realize automated prediction techniques that do not burden an operator with such tasks. We will also explore runtime techniques to mitigate the impact of burstiness and interference. Existing work has not focused on handling the adverse impact of service demand burstiness, i.e., user transaction patterns that cause sustained periods of high or low utilizations at system resources. Our initial work suggests that such burstiness can be tamed using fewer resources by intelligently reordering incoming transactions. We will build on this insight to realize new runtime scheduling techniques. As part of this theme, we will also exploit our ongoing work on interference detection to automatically scale cloud resource instances , e.g., containers, in response to interference. Existing approaches do not consider how individual transaction types get impacted by interference at a given instance. We will build models that can use such fine-grained information to intelligently distribute transactions to instances such that interference is mitigated using minimum instances.This program will expand the state of the art in data-driven performance prediction and management research. Canadian organizations can exploit the research to reduce costs related to poor performance and resource over-provisioning.