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Using Fault Model Enforcement (FME) to Improve Availability

Using Fault Model Enforcement (FME) to Improve Availability. EASY ’02 Workshop Kiran Nagaraja, Ricardo Bianchini, Richard Martin, Thu Nguyen Department of Computer Science Rutgers University. Motivation. Network services are extremely complex Typically many software and hardware components

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Using Fault Model Enforcement (FME) to Improve Availability

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  1. Using Fault Model Enforcement (FME) to Improve Availability EASY ’02 Workshop Kiran Nagaraja, Ricardo Bianchini, Richard Martin, Thu Nguyen Department of Computer Science Rutgers University

  2. Motivation • Network services are extremely complex • Typically many software and hardware components • Numerous fault points and types • E.g, nodes, disks, cables, links, switches, etc. • Extremely difficult for services to tolerate all these faults • Hard to reason about all possible faults • Difficult to determine actual fault • Many faults exhibit same runtime symptoms

  3. FME Approach • Define a reduced abstract fault model • Components, faults, symptoms, component behavior during faults • Enforce this fault model at run-time • If an “unexpected” fault occurs, map to one that was planned for in the abstract model • “If the facts don’t fit the theory, change the facts.” - Albert Einstein • Allow designer to concentrate on tolerating a well-defined, yet limited in complexity, set of faults

  4. Our Study • Estimate potential impact of FME • Have not yet implemented FME • Case study: PRESS cluster-based web server • PRESS has simple abstract fault model • In companion study, only achieve around three 9’s • Study hypothetical improvement if FME was used to enforce PRESS’s abstract fault model • FME can reduce the unavailability by up to 50%

  5. Outline • FME in more detail • Evaluation methodology • PRESS web server • Availability study • Related work • Conclusions • Future directions

  6. Fault Model Enforcement (FME) • Enforce a reduced fault model at runtime • Allow service to perform correct recovery action to regain full functionality • How to enforce a reduced fault model? • Two ideas so far • Map an unexpected fault to an expected fault • E.g., crash a node if the network link connecting it to the switch fails • Fail outer component if sub-component fails • E.g., crash a node if the disk fails • How is it different from fail-stop ? • Allows reasoning about failures at a desired abstraction

  7. Evaluation Methodology • Want to evaluate FME’s potential impact • Two phase methodology • Phase I - Single fault injection analysis • Define and inject faults on “live” system • Monitor system performance (throughput T) and availability(A) = fraction of successful requests • Phase II - Use an analytical model to determine performability • Computes average availability and average throughput

  8. Case Study: PRESS Web Server • Cluster-based, locality-conscious web server • Serve requests out of global memory pool • Exclusion from pool  lower performance • Simple fault model • Connection failure/lost heartbeats = node failure • Recovery through rejoin of “new” node • Several versions developed over time • TCP, VIA • Different fault detection mechanism • Heart-beat for TCP • Connection breaks for VIA

  9. Fault Set • Fault Load Link down Switch down SCSI timeout Node crash Node freeze Application crash Application hang • All faults are modeled as fail-stop

  10. PRESS with FME • Recovery upon fault model mismatch • Restart 0, 1 or all nodes? • FME approach: reboot the appropriate node after a fault and its recovery have occurred • Link down – reboot unreachable node • Switch down – reboot all nodes • Disk failure – reboot node with faulty disk • Node, application crash – do nothing

  11. Single-Fault Experiments • Setup: 4 PC cluster running at 90% load • 3 versions: TCP, TCP-HB, VIA • Use results to evaluate impact of FME

  12. Single Fault - Results Link Failure Application Hang

  13. Modeling – Seven Stage Model • Input: measured throughput and availability • Parameters: MTTF, MTTR, operator on site time • Output: average availability & average throughput

  14. Modeling Availability • Assumptions: • Effects of faults are independent • Fault arrivals are exponential • Overall unavailability = ΣT(unavailability of all faults)

  15. Modeling Results • Application fault rate: 1/month • Time to operator intervention: 5 minutes • Unavailability of TCP-HB reduced by ~50% • VIA: ~36% reduction

  16. Modeling Results • Application fault rate: 1/day - unstable s/w • Time to operator intervention: 5 minutes • Unavailability of TCP-HB reduces by > 50% • VIA: ~13% reduction

  17. Related Work • Enforcing fail-stop • Tandem Non-Stop – process pairs • Robust design with rigorous internal assertions • Fault detection and fail-over • HA-Linux • Reactive and proactive rejuvenation • Recursive restartability(ROC) – Berkeley & Stanford • Software rejuvenation – Duke

  18. Conclusion • FME allows for very simple fault models • FME can cut the unavailability by up to 50% • Fault detection mechanism is crucial for effectiveness • Benefits increase with fault coverage

  19. FME - Future Directions • How extensive should the fault model be? • Determines programming complexity/effort • How to prevent FME from reducing availability? • Bugs within enforcement? • When to declare a symptom a fault? • FME reduces human intervention • Are humans better at deciding? • 8-23 % of recovery procedures are botched [Brown 2001]

  20. Thank you. http://www.panic-lab.rutgers.edu/Projects/vivo

  21. Communication Architecture • All operations by main thread are non-blocking • Separate send, receive and multiple disk helper threads • Filling up of queues could stall the entire node

  22. Performability • Model computes 2 metrics: • Average throughput (AT) • Average Availability (AA) • Performability P = Tn x log(AI) log(AA) • AI : Availability of Ideal system with 99.999 • Log scale ratio allows a linear relationship with unavailability

  23. Experiments: Single-Fault Loads • 4 800Mhz PIII PCs, 206MB, 2x10000 SCSI disks, 1Gb/s cLan interconnect (TCP or VIA) • PRESS: 128MB file cache, static content • Clients: constant rate ~ 90% server capacity • Modified sclient [Banga 97] • Rutgers trace; file size = avg. request size

  24. Events Central Controller User-Level Daemon Process Ctrl Applications E.g. PRESS Mlib comLib glibc sys_calls n/w stack Kernel Node A Node B emulation SCSI n/w faults Node/OS Fast & Reliable SAN Mendosus – Fault Injection

  25. Phase II – Modeling Performability • 5 minutes duration for operator intervention(E) and restart(F) stages

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