Dependability in the cloud: Challenges and opportunities

Joshi, Kaustubh; Bunker, Guy; Jahanian, Farnam; Moorsel, Aad van; Weinman, Joseph B.

doi:10.1109/dsn.2009.5270350

Cited by 30 publications

(13 citation statements)

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“…Hosting of private data off-premise may furthermore raise a lot of questions about privacy and confidentiality of the client's data. Last but not least, for competitive reasons, providers might not want to disclose information about their actual service level [91]. The notion of a correct service is discussed thoroughly in Sect.…”

Section: Dependable Computingmentioning

confidence: 99%

Monitoring of Client-Cloud Interaction

Lampesberger

Rady

2015

Correct Software in Web Applications and Web Services

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When a client consumes a cloud service, computational liabilities are transferred to the service provider in accordance to the cloud paradigm, and the client loses some control over software components. One way to raise assurance about correctness and dependability of a consumed service and its software components is monitoring. In particular, a monitor is a system that observes the behavior of another system, and observation points that expose the target system's state and state changes are required. Due to the cloud paradigm, popular techniques for monitoring such as code instrumentation are often not available to the client because of limited visibility, lack of control, and black-box software components. Based on a literature review, we identify potential observation points in today's cloud services. Furthermore, we investigate two cloud-specific monitoring applications based on our ongoing research. While service level agreement (SLA) monitoring ensures that agreed-upon conditions between clients and providers are met, language-based anomaly detection monitors the interaction between client and cloud for misuse attempts.

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Section: Dependable Computingmentioning

confidence: 99%

Monitoring of Client-Cloud Interaction

Lampesberger

Rady

2015

Correct Software in Web Applications and Web Services

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“…They are changing dynamically as well due to the addition and removal of system components, changing execution environments and workloads, frequent updates and upgrades, online repairs and more. In such largescale complex and dynamic systems, failures are common [60], [36].…”

Section: Related Workmentioning

confidence: 99%

Exploring Time and Frequency Domains for Accurate and Automated Anomaly Detection in Cloud Computing Systems

Guan

DeBardeleben

et al. 2013

2013 IEEE 19th Pacific Rim International Symposium on Dependable Computing

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Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructures. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software faults and environmental factors. Autonomic anomaly detection is crucial for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect anomalous cloud behaviors, we need to monitor the cloud execution and collect runtime cloud performance data. For different types of failures, the data display different correlations with the performance metrics. In this paper, we present a wavelet-based multi-scale anomaly identification mechanism, that can analyze profiled cloud performance metrics in both time and frequency domains and identify anomalous cloud behaviors. Learning technologies are exploited to adapt the selection of mother wavelets and a sliding detection window is employed to handle cloud dynamicity and improve anomaly detection accuracy. We have implemented a prototype of the anomaly identification system and conducted experiments on an on-campus cloud computing environment. Experimental results show the proposed mechanism can achieve 93.3% detection sensitivity while keeping the false positive rate as low as 6.1% while outperforming other tested anomaly detection schemes.

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“…Currently, the research in cloud computing field focuses on functionality, performance, application, and energy consumption. Because of emerging fault events in cloud computing platforms, the research on improving the dependability of cloud computing is focused gradually [10].…”

Section: Related Workmentioning

confidence: 99%