Computing and Information Systems - Research Publications
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ItemOn the effectiveness of isolation-based anomaly detection in cloud data centers(WILEY, 2017-09-25)Summary The high volume of monitoring information generated by large‐scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource‐intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large‐scale environment such as clouds. Isolation‐based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation‐based anomaly detection method, to detect anomalies in large‐scale cloud data centers. We propose a method to code time‐series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time‐series and to be applied online and in real‐time.
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ItemMitigating impact of short-term overload on multi-cloud web applications through geographical load balancing(WILEY, 2017-06-25)Summary Managed by an auto‐scaler in the clouds, applications may still be overloaded by sudden flash crowds or resource failures as the auto‐scaler takes time to make scaling decisions and provision resources. With more cloud providers building geographically dispersed data centers, applications are commonly deployed in multiple data centers to better serve customers worldwide. In this case, instead of sufficiently over‐provisioning each data center to prepare for occasional overloads, it is more cost‐efficient to over‐provision each data center a small amount of capacity and to balance the extra load among them when resources in any data center are suddenly saturated. In this paper, we present a decentralized system that timely detects short‐term overload situations and autonomously handles them using geographical load balancing and admission control to minimize the resulted performance degradation. Our approach also includes a new algorithm that optimally distributes the excessive load to remote data centers causing minimum increase of overall response times. We developed a prototype and evaluated it on Amazon Web Services. The results show that our approach is able to maintain acceptable quality of service while greatly increase the number of requests served during overloading periods.
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ItemTowards autonomic detection of SLA violations in Cloud infrastructures(ELSEVIER, 2012-07)
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ItemA heuristic for mapping virtual machines and links in emulation testbeds(IEEE, 2009-12-01)
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ItemTowards self-managed adaptive emulation of grid environments(IEEE, 2009-11-19)