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Performability of Web-based Applications

Performability of Web-based Applications. PI: Katerina Goseva – Popstojanova Students: Ajay Deep Singh & Sunil Mazimdar Lane Dept. Computer Science and Electrical Engineering West Virginia University, Morgantown, WV katerina@csee.wvu.edu. Problem .

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Performability of Web-based Applications

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  1. Performability of Web-based Applications PI: Katerina Goseva – Popstojanova Students: Ajay Deep Singh & Sunil Mazimdar Lane Dept. Computer Science and Electrical Engineering West Virginia University, Morgantown, WV katerina@csee.wvu.edu

  2. Problem • World Wide Web is the biggest existing distributed system so far • Huge number of Web clients - tens of millions and rising • Users demand 24/7 availability and response time within several seconds • However, very often they experience long and unpredictable delays (WWW - World Wide Wait) • Problem: Traditional analysis and prediction methods do not work for Web

  3. Relevance to NASA Increasing use of Web-based technology at NASA • Web sites Agency wide • Control of daily mission operations from multiple geographically distributed locations via Internet (e.g., Web Interface for Telescience at JPL) • Real-time applications remotely controlled/monitored over the Internet or an Intranet (e.g., Tempest embedded Web server at Glenn Research Center)

  4. Relevance to NASA • Our empirical analysis is based on data extracted from actual Web logs of ten servers • Three public and three private Web servers at the NASA IV&V Facility • Lane Department of Computer Science and Electrical Engineering (CSEE) Web server • NASA Kennedy Space Center (NASA-KSC) Web server • Campus wide Web server at the University of Saskatchewan • Web server of the commercial Internet provider ClarkNet

  5. Approach Develop methods and tools that are general and powerful enough to provide flexible analysis and quality assurance of Web reliability, availability, and performance Develop scalable framework that combines measurements and models at different levels of detail and abstraction • Reliability/Availability: based on typical usage patterns • Performance: non-Poisson queuing theory • Combine reliability / availability and performance and analyze their tradeoffs

  6. Approach User session characterization Web access log analysis Realistic workload Session layer (user view) Performance model Software/hardware resource utilization Service layer (software architectural view) Performability model Application & hardware resource monitoring System layer (deployment view) Software/hardware failure/recovery characterization Reliability/ availability model Resource layer (hardware device view) Web error log analysis Request-based and session-based error characterization

  7. Workload analysis Intra-session characteristics Inter-session characteristics Web log files Errors and reliability analysis Unique errors (frequency & severity) Unique files with errors Request-based reliability Session-based reliability Accomplishments Create relational database

  8. 10-35% of the total number of errors are due only to 3 files Error rates have heavy tail distributions Accomplishments • Empirical analysis of the Web workload, errors, request-based and session-based reliability for ten Web servers • Some examples • Fixing the errors with the highest frequency of occurrence is the most cost effective way to improve Web quality

  9. Accomplishments We argue that session-based reliability is a better indicator of the users perception of the Web quality than request-based reliability

  10. Importance/benefits • Innovative theoretical and empirical research results • Introduced and empirically analyzed new measures for session-based workload and reliability • Conducted detailed empirical study on Web errors, including severity level of errors, unique errors, and unique files with errors – measures that have not been considered earlier • Practical value • The results of our research were actually used by Web administrators of the NASA IV&V and CSEE Web servers to improve their quality in a cost-effective way

  11. Next steps • Performance attributes • Develop non-Poisson queuing theory • Standard performance models (Queuing Networks & Layered Queuing Networks) assume Poisson arrivals • Web workload is bursty (highly non-Poisson) • Dependability attributes (reliability, availability) • Develop architecture-based models based on typical usage patterns • Combine performance and reliability / availability models • Analyze tradeoffsamong multiple quality attributes

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