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Terminology and empirical measures General methods to mask faults . Software-fault tolerance

FT 101 Jim Gray Microsoft Research http://research.microsoft.com/~gray/Talks/ 80% of slides are not shown (are hidden) so view with PPT to see them all Outline. Terminology and empirical measures General methods to mask faults . Software-fault tolerance Summary. Dependability: The 3 ITIES.

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Terminology and empirical measures General methods to mask faults . Software-fault tolerance

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  1. FT 101Jim GrayMicrosoft Researchhttp://research.microsoft.com/~gray/Talks/80% of slides are not shown (are hidden) so view with PPT to see them allOutline • Terminology and empirical measures • General methods to mask faults. • Software-fault tolerance • Summary

  2. Dependability: The 3 ITIES Security Integrity • Reliability / Integrity:does the right thing.(Also large MTTF) • Availability: does it now. (Also small MTTR MTTF+MTTRSystem Availability:if 90% of terminals up & 99% of DB up?(=>89% of transactions are serviced on time). • Holistic vs. Reductionist view Reliability Availability

  3. Unavailable (min/year) 50,000 5,000 500 50 5 .5 .05 Availability Class 1 2 3 4 5 6 7 System Type Unmanaged Managed Well Managed Fault Tolerant High-Availability Very-High-Availability Ultra-Availability High Availability System ClassesGoal: Build Class 6 Systems Availability 90.% 99.% 99.9% 99.99% 99.999% 99.9999% 99.99999% UnAvailability = MTTR/MTBF can cut it in ½ by cutting MTTR or MTBF

  4. Demo: looking at some nodes • Look at http://uptime.netcraft.com/ • Internet Node availability: 92% mean, 97% medianDarrell Long (UCSC)ftp://ftp.cse.ucsc.edu/pub/tr/ • ucsc-crl-90-46.ps.Z "A Study of the Reliability of Internet Sites" • ucsc-crl-91-06.ps.Z "Estimating the Reliability of Hosts Using the Internet" • ucsc-crl-93-40.ps.Z "A Study of the Reliability of Hosts on the Internet" • ucsc-crl-95-16.ps.Z "A Longitudinal Survey of Internet Host Reliability"

  5. Sources of Failures MTTF MTTR Power Failure: 2000 hr 1 hr Phone Lines Soft >.1 hr .1 hr Hard 4000 hr 10 hr Hardware Modules: 100,000hr 10hr (many are transient) Software: 1 Bug/1000 Lines Of Code (after vendor-user testing) => Thousands of bugs in System! Most software failures are transient: dump & restart system. Useful fact: 8,760 hrs/year ~ 10k hr/year

  6. Case Study - Japan"Survey on Computer Security", Japan Info Dev Corp., March 1986. (trans: Eiichi Watanabe). Vendor 4 2 % Tele Comm lines Vendor (hardware and software) 5 Months Application software 9 Months Communications lines 1.5 Years Operations 2 Years Environment 2 Years 10 Weeks 1,383 institutions reported (6/84 - 7/85) 7,517 outages, MTTF ~ 10 weeks, avg duration ~ 90 MINUTES To Get 10 Year MTTF, Must Attack All These Areas 1 2 % 1 1 . 2 Environment % 2 5 % Application Software 9 . 3 % Operations

  7. Case Studies - Tandem TrendsReported MTTF by Component 1985 1987 1990 SOFTWARE 2 53 33 Years HARDWARE 29 91 310 Years MAINTENANCE 45 162 409 Years OPERATIONS 99 171 136 Years ENVIRONMENT 142 214 346 Years SYSTEM 8 20 21 Years Problem: Systematic Under-reporting

  8. Many Software Faults are Soft After Design Review Code Inspection Alpha Test Beta Test 10k Hrs Of Gamma Test (Production) Most Software Faults Are Transient MVS Functional Recovery Routines 5:1 Tandem Spooler 100:1 Adams >100:1 Terminology: Heisenbug: Works On Retry Bohrbug: Faults Again On Retry Adams: "Optimizing Preventative Service of Software Products", IBM J R&D,28.1,1984 Gray: "Why Do Computers Stop", Tandem TR85.7, 1985 Mourad: "The Reliability of the IBM/XA Operating System", 15 ISFTCS, 1985.

  9. Summary of FT Studies • Current Situation: ~4-year MTTF => Fault Tolerance Works. • Hardware is GREAT (maintenance and MTTF). • Software masks most hardware faults. • Many hidden software outages in operations: • New Software. • Utilities. • Must make all software ONLINE. • Software seems to define a 30-year MTTF ceiling. • Reasonable Goal: 100-year MTTF. class 4 today=>class 6 tomorrow.

  10. Fault Tolerance vs Disaster Tolerance • Fault-Tolerance: mask local faults • RAID disks • Uninterruptible Power Supplies • Cluster Failover • Disaster Tolerance: masks site failures • Protects against fire, flood, sabotage,.. • Redundant system and service at remote site. • Use design diversity

  11. Outline • Terminology and empirical measures • General methods to mask faults. • Software-fault tolerance • Summary

  12. Fault Model • Failures are independentSo, single fault tolerance is a big win • Hardware fails fast (blue-screen) • Software fails-fast (or goes to sleep) • Software often repaired by reboot: • Heisenbugs • Operations tasks: major source of outage • Utility operations • Software upgrades

  13. Fault Tolerance Techniques • Fail fast modules: work or stop • Spare modules : instant repair time. • Independent module fails by design MTTFPair ~ MTTF2/ MTTR (so want tiny MTTR) • Message based OS: Fault Isolationsoftware has no shared memory. • Session-oriented comm: Reliable messagesdetect lost/duplicate messages coordinate messages with commit • Process pairs :Mask Hardware & Software Faults • Transactions: give A.C.I.D. (simple fault model)

  14. Example: the FT Bank Modularity & Repair are KEY: vonNeumann needed 20,000x redundancy in wires and switches We use 2x redundancy. Redundant hardware can support peak loads (so not redundant)

  15. Fail-Fast is Good, Repair is Needed Lifecycle of a module fail-fast gives short fault latency High Availability is low UN-Availability Unavailability ­ MTTR MTTF Improving either MTTR or MTTF gives benefit Simple redundancy does not help much.

  16. Hardware Reliability/Availability (how to make it fail fast) Comparitor Strategies: Duplex: Fail-Fast: fail if either fails (e.g. duplexed cpus) vs Fail-Soft: fail if both fail (e.g. disc, atm,...) Note: in recursive pairs, parent knows which is bad. Triplex: Fail-Fast: fail if 2 fail (triplexed cpus) Fail-Soft: fail if 3 fail (triplexed FailFast cpus)

  17. Redundant Designs have Worse MTTF! THIS IS NOT GOOD: Variance is lower but MTTF is worse Simple redundancy does not improve MTTF (sometimes hurts). This is just an example of the airplane rule. The Airplane Rule: A two-engine airplane has twice as many engine problems as a one engine plane.

  18. Add Repair: Get 104 Improvement

  19. When To Repair? Chances Of Tolerating A Fault are 1000:1 (class 3) A 1995 study: Processor & Disc Rated At ~ 10khr MTTF Computed Single Observed Failures Double Fails Ratio 10k Processor Fails 14 Double ~ 1000 : 1 40k Disc Fails, 26 Double ~ 1000 : 1 Hardware Maintenance: On-Line Maintenance "Works" 999 Times Out Of 1000. The chance a duplexed disc will fail during maintenance?1:1000 Risk Is 30x Higher During Maintenance => Do It Off Peak Hour Software Maintenance: Repair Only Virulent Bugs Wait For Next Release To Fix Benign Bugs

  20. OK: So Far Hardware fail-fast is easy Redundancy plus Repair is great (Class 7 availability) Hardware redundancy & repair is via modules. How can we get instant software repair? We Know How To Get Reliable Storage RAID Or Dumps And Transaction Logs. We Know How To Get Available Storage Fail Soft Duplexed Discs (RAID 1...N). ? How do we get reliable execution? ? How do we get available execution?

  21. Outline • Terminology and empirical measures • General methods to mask faults. • Software-fault tolerance • Summary

  22. Key Idea } { } { Architecture Hardware Faults Software Masks Environmental Faults Distribution Maintenance • Software automates / eliminates operators So, • In the limit there are only software & design faults.Software-fault tolerance is the key to dependability. INVENT IT!

  23. Software Techniques: Learning from Hardware Recall that most outages are not hardware. Most outages in Fault Tolerant Systems are SOFTWARE Fault Avoidance Techniques: Good & Correct design. After that: Software Fault Tolerance Techniques: Modularity (isolation, fault containment) Design diversity N-Version Programming: N-different implementations Defensive Programming: Check parameters and data Auditors: Check data structures in background Transactions: to clean up state after a failure Paradox: Need Fail-Fast Software

  24. Fail-Fast and High-Availability Execution Software N-Plexing: Design Diversity N-Version Programming Write the same program N-Times (N > 3) Compare outputs of all programs and take majority vote Process Pairs: Instant restart (repair) Use Defensive programming to make a process fail-fast Have restarted process ready in separate environment Second process “takes over” if primary faults Transaction mechanism can clean up distributed state if takeover in middle of computation.

  25. What Is MTTF of N-Version Program? First fails after MTTF/N Second fails after MTTF/(N-1),... so MTTF(1/N + 1/(N-1) + ... + 1/2) harmonic series goes to infinity, but VERY slowly for example 100-version programming gives ~4 MTTF of 1-version programming Reduces variance N-Version Programming Needs REPAIR If a program fails, must reset its state from other programs. => programs have common data/state representation. How does this work for Database Systems? Operating Systems? Network Systems? Answer: I don’t know.

  26. Why Process Pairs Mask Faults:Many Software Faults are Soft After Design Review Code Inspection Alpha Test Beta Test 10k Hrs Of Gamma Test (Production) Most Software Faults Are Transient MVS Functional Recovery Routines 5:1 Tandem Spooler 100:1 Adams >100:1 Terminology: Heisenbug: Works On Retry Bohrbug: Faults Again On Retry Adams: "Optimizing Preventative Service of Software Products", IBM J R&D,28.1,1984 Gray: "Why Do Computers Stop", Tandem TR85.7, 1985 Mourad: "The Reliability of the IBM/XA Operating System", 15 ISFTCS, 1985.

  27. Heisenbugs:A Probabilistic Approach to Availability There is considerable evidence that (1) production systems have about one bug per thousand lines of code (2) these bugs manifest themselves in stochastically: failures are due to confluence of rare events, (3) system mean-time-to-failure has a lower bound of a decade or so. To make highly available systems, architects must tolerate these failures by providing instant repair (un-availability is approximated by repair_time/time_to_fail so cutting the repair time in half makes things twice as good. Ultimately, one builds a set of standby servers which have both design diversity and geographic diversity. This minimizes common-mode failures.

  28. Process Pair Repair Strategy If software fault (bug) is a Bohrbug, then there is no repair “wait for the next release” or “get an emergency bug fix” or “get a new vendor” If software fault is a Heisenbug, then repair is reboot and retry or switch to backup process (instant restart) PROCESS PAIRS Tolerate Hardware Faults Heisenbugs Repair time is seconds, could be mili-seconds if time is critical Flavors Of Process Pair: Lockstep Automatic State Checkpointing Delta Checkpointing Persistent

  29. How Takeover Masks Failures Server Resets At Takeover But What About Application State? Database State? Network State? Answer: Use Transactions To Reset State! Abort Transaction If Process Fails. Keeps Network "Up" Keeps System "Up" Reprocesses Some Transactions On Failure

  30. PROCESS PAIRS - SUMMARY Transactions Give Reliability Process Pairs Give Availability Process Pairs Are Expensive & Hard To Program Transactions + Persistent Process Pairs => Fault Tolerant Sessions & Execution When Tandem Converted To This Style Saved 3x Messages Saved 5x Message Bytes Made Programming Easier

  31. SYSTEM PAIRSFOR HIGH AVAILABILITY Primary Backup Programs, Data, Processes Replicated at two sites. Pair looks like a single system. System becomes logical concept Like Process Pairs: System Pairs. Backup receives transaction log (spooled if backup down). If primary fails or operator Switches, backup offers service.

  32. SYSTEM PAIR CONFIGURATION OPTIONS Backup Primary Mutual Backup: each has1/2 of Database & Application Hub: One site acts as backup for many others In General can be any directed graph Stale replicas: Lazy replication Primary Primary Primary Backup Backup Primary Copy Copy Copy

  33. SYSTEM PAIRS FOR: SOFTWARE MAINTENANCE ( B a c k u p ) ( B a c k u p ) ( P r i m a r y ) ( P r i m a r y ) Similar ideas apply to: Database Reorganization Hardware modification (e.g. add discs, processors,...) Hardware maintenance Environmental changes (rewire, new air conditioning) Move primary or backup to new location. V 1 V 1 V 1 V 2 S t e p 1 : B o t h s y s t e m s a r e r u n n i n g V 1 . S t e p 2 : B a c k u p i s c o l d - l o a d e d a s V 2 . ( P r i m a r y ) ( P r i m a r y ) ( B a c k u p ) ( B a c k u p ) V 1 V 2 V 2 V 2 S t e p 4 : B a c k u p i s c o l d - l o a d e d a s V 2 D 3 0 . S t e p 3 : S W I T C H t o B a c k u p .

  34. SYSTEM PAIR BENEFITS Protects against ENVIRONMENT: weather utilities sabotage Protects against OPERATOR FAILURE: two sites, two sets of operators Protects against MAINTENANCE OUTAGES work on backup software/hardware install/upgrade/move... Protects against HARDWARE FAILURES backup takes over Protects against TRANSIENT SOFTWARE ERRORR Allows design diversity different sites have different software/hardware)

  35. Key Idea } { } { Architecture Hardware Faults Software Masks Environmental Faults Distribution Maintenance • Software automates / eliminates operators So, • In the limit there are only software & design faults. Many are HeisenbugsSoftware-fault tolerance is the key to dependability. INVENT IT!

  36. References Adams, E. (1984). “Optimizing Preventative Service of Software Products.” IBM Journal of Research and Development. 28(1): 2-14.0 Anderson, T. and B. Randell. (1979). Computing Systems Reliability. Garcia-Molina, H. and C. A. Polyzois. (1990). Issues in Disaster Recovery. 35th IEEE Compcon 90. 573-577. Gray, J. (1986). Why Do Computers Stop and What Can We Do About It. 5th Symposium on Reliability in Distributed Software and Database Systems. 3-12. Gray, J. (1990). “A Census of Tandem System Availability between 1985 and 1990.” IEEE Transactions on Reliability. 39(4): 409-418. Gray, J. N., Reuter, A. (1993). Transaction Processing Concepts and Techniques. San Mateo, Morgan Kaufmann. Lampson, B. W. (1981). Atomic Transactions. Distributed Systems -- Architecture and Implementation: An Advanced Course. ACM, Springer-Verlag. Laprie, J. C. (1985). Dependable Computing and Fault Tolerance: Concepts and Terminology. 15’th FTCS. 2-11. Long, D.D., J. L. Carroll, and C.J. Park (1991). A study of the reliability of Internet sites. Proc 10’th Symposium on Reliable Distributed Systems, pp. 177-186, Pisa, September 1991. Darrell Long, Andrew Muir and Richard Golding, ``A Longitudinal Study of Internet Host Reliability,'' Proceedings of the Symposium on Reliable Distributed Systems, Bad Neuenahr, Germany: IEEE, September 1995, pp. 2-9

  37. Scaleable Replicated Databases Jim Gray (Microsoft) Pat Helland (Microsoft) Dennis Shasha (Columbia) Pat O’Neil (U.Mass)

  38. Outline • Replication strategies • Lazy and Eager • Master and Group • How centralized databases scale • deadlocks rise non-linearly with • transaction size • concurrency • Replication systems are unstable on scaleup • A possible solution

  39. Scaleup, Replication, Partition • N2more work

  40. Why Replicate Databases? • Give users a local copy for • Performance • Availability • Mobility (they are disconnected) • But... What if they update it? • Must propagate updates to other copies

  41. Propagation Strategies • Eager: Send update right away • (part of same transaction) • N times larger transactions • Lazy: Send update asynchronously • separate transaction • N times more transactions • Either way • N times more updates per second per node • N2 times more work overall

  42. Update Control Strategies • Master • Each object has a master node • All updates start with the master • Broadcast to the subscribers • Group • Object can be updated by anyone • Update broadcast to all others • Everyone wants Lazy Group: • update anywhere, anytime, anyway

  43. Quiz Questions: Name One • Eager • Master: N-Plexed disks • Group: ? • Lazy • Master: Bibles, Bank accounts, SQLserver • Group: Name servers, Oracle, Access... • Note: Lazy contradicts Serializable • If two lazy updates collide, then ... reconcile • discard one transaction (or use some other rule) • Ask for human advice • Meanwhile, nodes disagree => • Network DB state diverges: System Delusion

  44. Anecdotal Evidence • Update Anywhere systems are attractive • Products offer the feature • It demos well • But when it scales up • Reconciliations start to cascade • Database drifts “out of sync” (System Delusion) • What’s going on?

  45. Outline • Replication strategies • Lazy and Eager • Master and Group • How centralized databases scale • deadlocks rise non-linearly • Replication is unstable on scaleup • A possible solution

  46. Simple Model of Waits DBsize records • TPS transactions per second • Each • Picks Actions records uniformly from set of DBsize records • Then commits • About Transactions x Actions/2 resources locked • Chance a request waits is • Action rate is TPS x Actions • Active Transactions TPS x Actions x Action_Time • Wait Rate = Action rate x Chance a request waits • = • 10x more transactions, 100x more waits TransctionsxActions 2 Transactions x Actions 2 x DB_size TPS2x Actions3x Action_Time 2 x DB_size

  47. Simple Model of Deadlocks • A deadlock is a wait cycle • Cycle of length 2: • Wait rate x Chance Waitee waits for waiter • Wait rate x (P(wait) / Transactions) • Cycles of length 3 are PW3, so ignored. • 10x bigger trans = 100,000x more deadlocks TPS x Actions3x Action_Time 2 x DB_size TPS x Actions x Action_Time TPS2x Actions3x Action_Time 2 x DB_size TPS2x Actions5x Action_Time 4 x DB_size2

  48. Summary So Far • Even centralized systems unstable • Waits: • Square of concurrency • 3rd power of transaction size • Deadlock rate • Square of concurrency • 5th power of transaction size Trans Size Concurrency

  49. Outline • Replication strategies • How centralized databases scale • Replication is unstable on scaleup • Eager (master & group) • Lazy (master & group & disconnected) • A possible solution

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