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![Page 1: What Bugs Live in the Cloud? A Study of 3000+ Issues in Cloud Systems Jeffry Adityatama, Kurnia J. Eliazar, Agung Laksono, Jeffrey F. Lukman, Vincentius.](https://reader035.fdocuments.us/reader035/viewer/2022062321/56649db65503460f94aa83e5/html5/thumbnails/1.jpg)
What Bugs Live in the Cloud?A Study of 3000+ Issues in Cloud Systems
Jeffry Adityatama, Kurnia J. Eliazar, Agung Laksono, Jeffrey F. Lukman, Vincentius Martin, and Anang D. Satria
Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, Tiratat Patana-anake
Thanh Do
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2
First, let’s ask Google
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Cloud era
No Deep Root Causes…
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What reliability research community do?
• Bug study1. A Study of Linux File System Evolution. In FAST ’13. 2. A Comprehensive Study on Real World Concurrency Bug
Characteristics. In ASPLOS ’08. 3. Precomputing Possible Configuration Error Diagnoses. In ASE
’11. …
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Open sourced cloud software
• Publicly accessible bug repositories
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Study to solve…
• What bugs “live” in the cloud?• Are there new classes of bugs unique to cloud
systems?• How should cloud dependability tools evolve
in near future?• Many others questions…
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Cloud Bug Study (CBS)
• 6 systems: Hadoop MapReduce, HDFS, HBase, Cassandra, Zookeeper, and Flume
• 11 people, 1 year study• Issues in a 3-year window:
Jan 2011 to Jan 2014• ~21000 issues reviewed• ~3600 “vital” issues in-depth study• Cloud Bug Study (CBS) database
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Classifications
• Aspects – Reliability, performance, availability, security, consistency, scalability, topology, QoS
• Hardware failures - types of hardware and types of hardware failures
• Software bug types – Logic, error handling, optimization, config, race, hang, space, load
• Implications – Failed operation, performance, component down- time, data loss, data staleness, data corruption
• ~25000 annotations in total, about 7 annotations per issue
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Cloud Bug Study (CBS) database
• Open to public
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Outline
• Introduction• Methodology• Overview of results• Other CBS database use cases• Conclusion
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Methodology
• 6 systems, 3-year span, 2011 to 2014• 20~30 bugs a day! Protein yeah!• 17% “vital” issues affecting
real deployments• 3655 vital issues
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Example issueTitle
Type & Priority
Description
Time to resolve
Discussion
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Outline
• Introduction• Methodology• Overview of results• Other CBS database use cases• Conclusion
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Classifications for each vital issue
• Aspects• Hardware types and failure modes• Software bug types• Implications• Bug scopes
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Overview of result
• Aspects • Hardware faults vs. Software faults• Implications
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Aspects
• CS = Cassandra• FL = flume• HB = HBase• HD = HDFS• MR = MapReduce• ZK = ZooKeeper
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Aspects: Reliability
• Reliability (45%)– Operation & job
failures/errors, data loss/corruption/staleness
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Aspects: Performance
• Reliability• Performance (22%)
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Aspects: Availability
• Reliability• Performance• Availability (16%)– Node and cluster
downtime
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Aspects: Security
• Reliability• Performance• Availability• Security (6%)
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Overview of result
• Aspects (classical)• Aspects – Data consistency, scalability, topology, QoS
• Hardware faults vs. Software faults• Implications
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Aspects: Data consistency
• Data consistency (5%)– Permanent inconsistent
replicas– Various root causes:• Buggy operational
protocol• Concurrency bugs
and node failures
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Cassandra cross-DC synchronization
A’
B’ B
C’
A
C
Background operational protocols often buggy!
A’ A’
B’ B’
C’ Permanent inconsistency
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Aspects: Scalability
• Data consistency• Scalability (2%)– Small number does not
mean not important!– Only found at scale
• Large cluster size• Large data• Large load• Large failures
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Large cluster• In Cassandra
O(n3) calculation
Ring position changed.
100x
CPU explosion
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Large data
In HBase
Tens ofminutes
R1
R2
R3
R…
R100K
Insufficient lookup operation
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Large load
In HDFS 1000x small files in parallel
…
…
… Not expecting small files!
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Large failure
Time cost: 7+ hours
AM managing 16,000 tasks fails
1
2
3
…
1K
2K
3K
4K
5K
…
16K
Un-optimized connection
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From above examples…
• Protocol algorithms must anticipate – Large cluster sizes– Large data– Large request load of various kinds– Large scale failures
• The need for scalability bug detection tools
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Aspects: Topology
• Data consistency• Scalability• Topology (1%)– Systems have problem
when deployed on some network topology• Cross DC• Different racks• New layering architecture
– Typically unseen in pre-deployment
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Aspects: QoS
• Data consistency• Scalability• Topology• QoS (1%)– Fundamental for multi-
tenant systems– Two main points
• Horizontal/intra-system QoS
• Vertical/cross-system QoS
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Overview of result
• Aspects (classical)• Aspects (unique)– Data consistency, scalability, topology, QoS
• Hardware faults vs. Software faults• Implications
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HW faults vs. SW faults“Hardware can fail, and reliability should come from software.”
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HW faults and modes
• 299 improper handling of node fail-stop failure
• A 25% normal speed memory card causes problems in HBase deployment.
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Hardware faults vs. Software faults
• Hardware failures, components and modes• Software bug types
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Software bug types: Logic
• Logic (29%)– Many domain-specific
issues
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Software bug types: Error handling
• Logic• Error handling (18%)– Aspirator, Yuan et al,
[OSDI’ 14]
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Software bug types: Optimization
• Logic• Error handling• Optimization (15%)
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Software bug types: Configuration
• Logic• Error handling• Optimization• Configuration (14%)
– Automating Configuration Troubleshooting. [OSDI ’10]
– Precomputing Possible Configuration Error Diagnoses. [ASE ’11]
– Do Not Blame Users for Misconfigurations. [SOSP ’13]
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Software bug types: Race
• Race (12%)– < 50% local concurrency
bugs• Buggy thread interleaving• Tons of work
– > 50% distributed concurrency bugs• Reordering of messages,
crashes, timeouts• More work is needed
– SAMC [OSDI ’14]
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Software bug types: Hang
• Hang (4%)– Classical deadlock– Un-served jobs, stalled
operations, …• Root causes?• How to detect them?
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Software bug types: Space
• Space (4%)– Big data + leak = Big leak– Clean-up operations
must be flawless.
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Software bug types: Load
• Load (4%)– Happen when systems
face high request load– Relates to QoS and
admission control
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Overview of result
• Aspects (classical)• Aspects (unique)– Data consistency, scalability, topology, QoS
• Hardware faults vs. Software faults• Implications
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Implications
• Failed operation (42%)• Performance (23%)• Downtimes (18%)• Data loss (7%)• Data corruption (5%)• Data staleness (5%)
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Root causesEvery implication can be caused by all kinds of hardware and software faults!
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“Killer” bugs
• Bugs that simultaneously affect multiple nodes or even the entire cluster
• Single Point of Failure still exists in many forms– Positive feedback loop – Buggy failover – Repeated bugs after failover – …
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Outline
• Introduction• Methodology• Overview of results• Other CBS database use cases• Conclusion
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CBS database
• 50+ per system and aggregate graphs from mining CBS database in the last one year
• Still more waiting to be studied…
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Components with most issuesHow should we enhance reliability for multiple cloud system interaction?
Cross-system issues are prevalent!
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Most challenging types of issues
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Top k% of most complicated issue
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System evolution
Hadoop 2.0
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Conclude
• One of the largest bug studies for cloud systems
• Many interesting findings, but more questions can be raised from our analysis– What types of performance issues exist?– Root causes for hang issues?– …
• Cloud Bug Study(CBS) database.
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Thank you!
http://ucare.cs.uchicago.edu/