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  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

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

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%

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

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

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

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

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

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

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

Single Fault - Results Link Failure Application Hang

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

Modeling Availability  Assumptions:  Effects of faults are independent  Fault arrivals are exponential  Overall unavailability = Σ T (unavailability of all faults)

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

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

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

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

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]

Thank you.

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

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  Log scale ratio allows a linear relationship with unavailability

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

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

Phase II – Modeling Performability  5 minutes duration for operator intervention(E) and restart(F) stages FaultMTTFMTTR Link down6 months3 minutes Switch down1 year1 hour SCSI timeout1 year1 hour Node crash2 weeks3 minutes Node freeze2 weeks3 minutes Application Crash2 months3 minutes Application Hang2 months3 minutes