Analysis of failures in the Tandem NonStop-UX Operating System

Thakur, A.; Iyer, Ravishankar K.; Young, Larry; Lee, I.

doi:10.1109/issre.1995.497642

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…The past decade has produced several studies in root-cause analysis for Operating Systems (OS) ranging from Guardian OS and Tandem Non-Stop UX OS to VAX/VMS and Windows NT [6,8,10,14,15,16,17]. In this progression, our study of Windows' crash data gauges the evolution of PC reliability.…”

Section: Related Researchmentioning

confidence: 99%

Crash Data Collection: A Windows Case Study

Ganapathi

Patterson

2005 International Conference on Dependable Systems and Networks (DSN'05)

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Reliability is a rapidly growing concern in contemporary Personal Computer (PC) industry, both for computer users as well as product developers. To improve dependability, systems designers and programmers must consider failure and usage data for operating systems as well as applications. In this paper, we discuss our experience with crash and usage data collection for Windows machines. We analyze results based on crashes in the UC Berkeley EECS department.

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Section: Related Researchmentioning

confidence: 99%

Crash Data Collection: A Windows Case Study

Ganapathi

Patterson

2005 International Conference on Dependable Systems and Networks (DSN'05)

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“…Tang [25] analyzed error logs pertaining to a multicomputer environment based on VAX/VMS cluster. Thakur [27] presented an analysis of failures in the Tandem Nonstop-UX operating system. Hsueh [9] explored errors and recovery in IBM's MVS operating system.…”

Section: Error and Failure Analysis Of A Lan Of Windows Nt-based Serversmentioning

confidence: 99%

Measurement-Based Analysis of System Dependability Using Fault Injection and Field Failure Data

Iyer

Kalbarczyk

2002

Performance Evaluation of Complex Systems: Techniques and Tools

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Abstract. The discussion in this paper focuses on the issues involved in analyzing the availability of networked systems using fault injection and the failure data collected by the logging mechanisms built into the system. In particular we address: (1) analysis in the prototype phase using physical fault injection to an actual system. We use example of fault injection-based evaluation of a software-implemented fault tolerance (SIFT) environment (built around a set of self-checking processes called ARMORS) that provides error detection and recovery services to spaceborne scientific applications and (2) measurement-based analysis of systems in the field. We use example of LAN of Windows NT based computers to present methods for collecting and analyzing failure data to characterize network system dependability. Both, fault injection and failure data analysis enable us to study naturally occurring errors and to provide feedback to system designers on potential availability bottlenecks. For example, the study of failures in a network of Windows NT machines reveals that most of the problems that lead to reboots are software related and that though the average availability evaluates to over 99%, a typical machine, on average, provides acceptable service only about 92% of the time.

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“…Thakur [21] presented an analysis of failures in the Tandem Nonstop-UX operating system. The study analyzed problems that resulted in panics and system crashes.…”

Section: Related Workmentioning

confidence: 99%

Networked Windows NT system field failure data analysis

Kalbarczyk

Iyer³

Proceedings 1999 Pacific Rim International Symposium on Dependable Computing

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This paper presents a measurement-based dependability study of a Networked Windows NT system based on field data collected from NT System Logs from 503 servers running in a production environment over a four-month period. The event logs at hand contains only system reboot information. We study individual server failures and domain behavior in order to characterize failure behavior and explore error propagation between servers. The key observations from this study are: (1) system software and hardware failures are the two major contributors to the total system downtime (22% and 10%), (2) recovery from application software failures are usually quick, (3) in many cases, more than one reboots are required to recover from a failure, (4) the average availability of an individual server is over 99%,(5) there is a strong indication of error dependency or error propagation across the network, (6) most (58%) reboots are unclassified indicating the need for better logging techniques, (7) maintenance and configuration contribute to 24% of system downtime.

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Analysis of failures in the Tandem NonStop-UX Operating System

Cited by 11 publications

References 11 publications

Crash Data Collection: A Windows Case Study

Crash Data Collection: A Windows Case Study

Measurement-Based Analysis of System Dependability Using Fault Injection and Field Failure Data

Networked Windows NT system field failure data analysis

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