Transcript Heisenbugs: A Probabilistic Approach to Availability Jim Gray Microsoft Research http://research.microsoft.com/~gray/Talks/ ½ the slides are not shown (are hidden, so view with PPT to.

Heisenbugs:
A Probabilistic Approach to Availability
Jim Gray Microsoft Research
http://research.microsoft.com/~gray/Talks/
½ the 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
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.
Dependability: The 3 ITIES
• Reliability / Integrity:
does the right thing.
(Also large MTTF)
Integrity Security
Reliability
• Availability: does it now.
(Also small MTTR
MTTF+MTTR
Availability
System Availability:
if 90% of terminals up & 99% of DB up?
(=>89% of transactions are serviced on time).
• Holistic vs. Reductionist view
High Availability System Classes
Goal: Build Class 6 Systems
System Type
Unavailable
(min/year)
50,000
Unmanaged
5,000
Managed
500
Well Managed
50
Fault Tolerant
5
High-Availability
.5
Very-High-Availability
.05
Ultra-Availability
Availability
90.%
99.%
99.9%
99.99%
99.999%
99.9999%
99.99999%
Availability
Class
1
2
3
4
5
6
7
UnAvailability = MTTR/MTBF
can cut it in ½ by cutting MTTR or MTBF
Demo: looking at some nodes
• Look at http://httpmonitor/
• Internet Node availability:
92% mean,
97% median
Darrell 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"
Sources of Failures
Power Failure:
Phone Lines
Soft
Hard
Hardware Modules:
Software:
MTTF
2000 hr
MTTR
1 hr
>.1 hr
4000 hr
100,000hr
.1 hr
10 hr
10hr (many are transient)
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
Case Studies - Tandem Trends
Reported MTTF by Component
Mean Time to S ystem Failure (years)
by Cause
450
400
maintenance
350
300
250
hardware
environment
200
operations
150
100
software
50
total
0
1985
1987
1989
1985
1987
1990
SOFTWARE
HARDWARE
MAINTENANCE
OPERATIONS
ENVIRONMENT
2
29
45
99
142
53
91
162
171
214
33
310
409
136
346
Years
Years
Years
Years
Years
SYSTEM
8
20
21
Years
Problem: Systematic Under-reporting
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
Tandem Spooler
Adams
5:1
100:1
>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.
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.
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
Outline
• Terminology and empirical measures
• General methods to mask faults.
• Software-fault tolerance
• Summary
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 Isolation
software has no shared memory.
• SESSION-ORIENTED COMM: Reliable messages
detect lost/duplicate messages
coordinate messages with commit
• PROCESS PAIRS :Mask Hardware & Software Faults
• TRANSACTIONS: give A.C.I.D. (simple fault model)
Example: the FT Bank
Fault Tolerant Computer
Backup System
System MTTF >10 YEAR (except for power & terminals)
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)
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.
Outline
• Terminology and empirical measures
• General methods to mask faults.
• Software-fault tolerance
• Summary
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!
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
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
LOGICAL PROCESS = PROCESS PAIR
if takeover in middle of computation.
SESSION
PRIMARY
PROCESS
STATE
INFORMATION
BACKUP
PROCESS
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.
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
Tandem Spooler
Adams
5:1
100:1
>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.
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”
LOGICAL PROCESS = PROCESS PAIR
SESSION
If software fault is a Heisenbug,
then repair is
PRIMARY
PROCESS
STATE
INFORMATION
BACKUP
PROCESS
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
How Takeover Masks Failures
Server Resets At Takeover But What About
LOGICAL PROCESS = PROCESS PAIR
SESSION
PRIMARY
PROCESS
Application State?
Database State?
Network State?
STATE
INFORMATION
BACKUP
PROCESS
Answer:
Use Transactions To Reset State!
Abort Transaction If Process Fails.
Keeps Network "Up"
Keeps System "Up"
Reprocesses Some Transactions On Failure
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
SYSTEM PAIRS
FOR 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.
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)
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 Heisenbugs
{
}
{
Software-fault tolerance is the key to dependability.
INVENT IT!
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