Computing and Information Systems - Theses

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End date: 2019 × Start date: 2019 × Type: PhD thesis × Subject: Anomaly Detection × Subject: Cloud Computing ×

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    Anomaly-aware Management of Cloud Computing Resources
    Kardani Moghaddam, Sara ( 2019)
    Cloud computing supports on-demand provisioning of resources in a virtualized, shared environment. Although virtualization and elasticity characteristics of cloud resources make this paradigm feasible, however, without efficient management of resources, the cloud system’s performance can degrade substantially. Efficient management of resources is required due to the inherent dynamics of cloud environment such as workload changes or hardware and software functionality such as hardware failures and software bugs. In order to meet the performance expectations of users, a comprehensive understanding of the performance dynamics and proper management actions is required. With the advent of data analysis techniques, this goal can be achieved by analyzing large volumes of monitored data for discovering abnormalities in the performance data. This thesis focuses on the anomaly aware resource scaling mechanisms which utilize anomaly detection techniques and resource scaling mechanism in the cloud to improve the performance of the system in terms of the quality of service and utilization of resources. It demonstrates how anomaly detection techniques can help to identify abnormalities in the behaviour of the system and trigger relevant resource reconfiguration actions to reduce the performance degradations in the application. The thesis advances the state-of-the-art in this field by making following contributions: 1. A taxonomy and comprehensive survey on performance analysis frameworks in the context of cloud resource management. 2. An Isolation-based anomaly detection module to identify performance anomalies in web based applications considering cloud dynamics. 3. An Isolation based iterative feature refinement to remove unrelated and noisy features to reduce the complexity of anomaly detection process in high-dimensional data. 4. A joint anomaly aware resource scaling mechanism for cloud hosted application. The approach tries to identify both the anomaly event and the root cause of the problem and trigger proper vertical and horizontal scaling actions to avoid or reduce performance degradations. 5. An adaptive Deep Reinforcement Learning (DRL) based scaling framework which leverages the knowledge of anomaly detection module to decide on proper decision making epochs. The scaling actions are encoded in DRL action space and the knowledge of actions values are obtained by training multi-layer Neural Networks.