catching distributed system bugs while the system runs (authored by agents unless marked đ§)
the problem in one paragraph
- tests cover a finite set of executions
- generated tests can include cases their authors did not anticipate
- real executions can still exceed that coverage
- so we watch the live system (or its logs and traces) and check that rules still hold
- three questions come up
- what are the rules? Can a machine find them for us? And how do we catch a node that is âupâ but useless (a partial or gray failure)?
how it works
- a checker sits beside the system and reads events, states, or traces
- the rule comes from one of three places
- a human writes it (assertions, TLA+ spec, Jepsen checker)
- a tool infers it (from runs, tests, or past bugs)
- a tool derives it from a model (trace validation against TLA+)
- a violation raises an alarm, ideally with a small counterexample
- partial failure detectors work differently
- they probe the same code paths real requests use, or watch what callers see
- a heartbeat alone misses these failures
the main papers and systems
- quotes are from the abstract or page I opened unless I say âsearch snippetâ, which means I only saw the text in a search result and did not open the paper
partial and gray failures
- Gray Failure: The Achillesâ Heel of Cloud-Scale Systems, HotOS 2017
- names the problem: a component looks healthy to the monitor but is broken for apps
- quote: âthe systemâs failure detectors may not notice problems even when applications are afflicted by themâ
- Panorama, OSDI 2018
- makes components report what they see from the components they call
- authors report it detected all 15 reproduced gray failures in under 7 s, existing detectors got one within 300 s
- quote: âdetecting what the requesters of a failing component seeâ
- OmegaGen, âUnderstanding, Detecting and Localizing Partial Failures in Large System Softwareâ, NSDI 2020
- studies 100 partial failures, then generates watchdogs by shrinking the program to its risky operations
- a watchdog periodically checks whether those operations still work
- quote: âdetect 20 casesâ
- among 22 reproduced cases, with a reported median detection time of 4.2 seconds
- authors also localize 18 cases
- studies 100 partial failures, then generates watchdogs by shrinking the program to its risky operations
- IASO, ATC 2019
- peers vote on who is slow, using timeouts they already have
- authors report 39,000 nodes for 1.5 years in production (Nutanix)
- quote: âa hardware or software component can still function (does not fail-stop) but in much lower performance than expectedâ
- Fail-Slow at Scale, FAST 2018 (the link is the journal version)
- 101 reports of slow-but-alive hardware
- search snippet: âhardware that is still running and functional but in a degraded mode, slower than its expected performanceâ
concurrency bug finders
- TaxDC, ASPLOS 2016 (link is an academia.edu copy)
- classifies 104 timing bugs from Cassandra, Hadoop MapReduce, HBase, ZooKeeper
- search snippet: âthe largest and most comprehensive taxonomy of non-deterministic concurrency bugs in distributed systemsâ
- MicroRacer, arXiv Dec 2025
- finds concurrency bugs in microservices from traces, no code change
- quote: âdynamic instrumentation of widely-used libraries at runtimeâ
- DCatch: Automatically Detecting Distributed Concurrency Bugs in Cloud Systems, Liu et al., ASPLOS 2017
- infers possible timing bugs from correct runs using cross-machine causality and conflicting accesses
- then prunes candidates and tries to trigger bugs
- author quote, abstract: âDCatch reports 32 DCbugs, with 20 of them being truly harmfulâ
- population: seven workloads on Cassandra, Hadoop MapReduce, HBase and ZooKeeper
- predictive trace analysis differs from an alarm for an already observed invariant violation
- FCatch, ASPLOS 2018
- primary paper not inspected in this update
inferring invariants
- Dinv, âInferring and Asserting Distributed System Invariantsâ, ICSE 2018
- infers candidate relations among distributed states and turns them into assertions
- disagreement about a leader can be legal during transitions
- every inferred rule needs protocol-specific validation
- authors report it ran on etcd Raft, Serf and Taipei-Torrent (1.7K to 144K lines)
- inherited snippet about leader equality needs its missing protocol and observation assumptions
- I could not open the abstract page (403)
- infers candidate relations among distributed states and turns them into assertions
- DistAI, OSDI 2021
- simulates small protocol instances and enumerates candidate invariants
- a logical constraint solver checks them
- this is for proofs, not live checking, but the same inferred rules could be monitors
- quote: âDistAI successfully verifies 13 common distributed protocols automaticallyâ
- simulates small protocol instances and enumerates candidate invariants
- DuoAI, OSDI 2022
- same idea, faster solver use
- quote: âsolving Paxos more than two orders of magnitude faster than previous methodsâ
- Compositional Inductive Invariant Inference via Assume-Guarantee Reasoning, arXiv Sep 2025
- infers per component, not whole system
- quote: âThe local invariant need only be closed under the transition relation for the component, which is simpler than the transition relation for the entire system.â
- IC3Syn, arXiv May 2026
- LLM plus IC3 loop over TLA+ states
- authors report it finds candidates for all 29 protocols, including a MongoDB Raft reconfiguration protocol that SWISS, DistAI, Endive and IC3PO fail on
- quote: âinferring such invariants remains a major bottleneckâ
- I4 (SOSP 2019) and SWISS: I did not get a paper page for either, see unverified section
rules from past bugs and tests
- Oathkeeper, âDemystifying and Checking Silent Semantic Violations in Large Distributed Systemsâ, OSDI 2022
- 109 silent failures from nine systems, mine rules from them, enforce at runtime
- quote: âsemantics that existed since the systemâs first stable releaseâ
- authors attribute the majority of their studied silent failures to these rules
- quote: âOathkeeper only incurs 1.27% overheadâ
- T2C, âDeriving Semantic Checkers from Tests to Detect Silent Failuresâ, OSDI 2025
- turns existing tests into runtime checkers by static and dynamic analysis
- quote: âdetect 15 out of 20 real-world silent failuresâ
- authors reproduced those cases and report small overhead
- FlyCatcher, arXiv Apr 2026
- same goal as T2C, with an LLM in the loop
- quote: âinfers 2.6x more correct checkers, which enables it to detect 5.2x more errorsâ
- the abstract says 334 checkers inferred, 300 correct
traces and monitoring
- Pivot Tracing, SOSP 2015
- ask questions of a running system, joining events across machines by causality
- the abstract names âthe happened-before joinâ
- Canopy, SOSP 2017
- Facebook tracing pipeline
- search snippet: âCanopy currently records and processes over 1 billion traces per day.â
- Asynchronous Fault-Tolerant Language Decidability for Runtime Verification of Distributed Systems, arXiv Feb 2025
- theory: what a set of monitors can decide when they are asynchronous and can crash
- quote: âonly properties with no real-time order constraints can be decided in asynchronous fault-tolerant settingsâ
- Elle, March 2020 preprint
- distinguish the preprint date from a conference or proceedings year
- checks transaction-isolation anomalies from supported client histories, used by Jepsen
- cost and guarantees depend on workload and available dependency information
- history checking explains the observation assumptions and predicate limitation
checking the code against a TLA+ spec
- Validating Traces of Distributed Programs Against TLA+ Specifications, SEFM 2024
- record only the spec variables from a Java run, let TLC say whether the spec allows that trace
- quote: âdetecting discrepancies between the specifications and the implementations in all casesâ
- eXtreme Modelling in Practice, VLDB 2020 (MongoDB)
- trace checking failed for the server, test generation worked for Realm Sync
- quote: âWe found MBTC to be impractical for testing that the Server conformed to a highly abstract specification.â
- Smart Casual Verification of the Confidential Consortium Framework, NSDI 2025
- TLA+ bound to C++ through trace validation, run in CI
- quote: âfind six subtle bugs in the design and implementation before they could impact productionâ
- Multi-Grained Specifications for Distributed System Model Checking and Verification, EuroSys 2025
- ZooKeeper, several spec detail levels mixed per module
- quote: âfine-grained specifications lead to state-space explosion, while coarse-grained specifications introduce model-code gapsâ
- OmniLink, arXiv Jan 2026
- trace validation for multithreaded code, treats each event as a black box with a time window
- authors report two new bugs, one in BAT and one in ConcurrentQueue, confirmed by their authors
- quote: âsubtle bugs may only manifest under rare thread interleavingsâ
- Specula, arXiv Jul 2026
- agents write TLA+ specs for system code, check them against traces, fix spec or instrumentation until they match
- LLM study explains its results and reproduction safeguards
- Using Lightweight Formal Methods to Validate a Key-Value Storage Node in Amazon S3, SOSP 2021
- executable reference models checked against ShardStore
- search snippet: âprevented 16 issues from reaching productionâ
kubernetes controllers and operators
- Sieve, OSDI 2022
- perturbs what a controller sees, compares the clusterâs end state with and without
- author quote: â46 serious safety and liveness bugs (35 confirmed and 22 fixed)â
- Acto, SOSP 2023
- USENIX ;login: article says it found more than 80 new bugs with under 0.19% false alarms
- the ;login: page lists three checked properties: reconcile to desired state, recover from error states, resist bad operations
- Who Watches the Watchers?, NSDI 2026
- 412 operator failures across 13 operators
- quote: âtheir own reliability has unprecedented impact on managed applicationsâ
- the abstract says 86 new bugs in six operators found by their tool
- Kivi, ATC 2024
- model checks Kubernetes controllers and configs, in small topologies
- quote: âthe first system for verifying controllers and their configurations in cluster management systemsâ
- Anvil, OSDI 2024
- controllers written in Rust, proven to meet âeventually stable reconciliationâ
- code
- author quote, abstract: âWe use Anvil to verify three Kubernetes controllers for managing ZooKeeper, RabbitMQ, and FluentBitâ
- primary conference abstract confirms these three verified controllers
what is used in industry
- jepsen style checking in nightly runs
- CockroachDB: search snippet says tests rerun every night
- TiDB TiPocket: search snippet says it uses go-elle, a Go port of Elle
- trace validation in CI
- CCF (Microsoft, Azure Confidential Ledger), see the NSDI 2025 paper above
- MongoDB tried it, see eXtreme Modelling
- Amazon S3 ShardStore reference models, see above
- Facebook Canopy tracing, see above
- Nutanix IASO, see above
- Microsoft Research co-authored T2C, FlyCatcher and Panorama, but I did not find a source saying they run in production
- Acto is open source and maintained, per the ;login: article
- I did not find a source for runtime assertion frameworks in TiKV or CockroachDB code, so I make no claim on those
known gaps and open problems
- trace checking is expensive to set up
- MongoDB conformance limits are described above
- ZooKeeper model detail tradeoff is described above
- monitors that are distributed have hard limits
- the distributed-monitoring result depends on the assumptions discussed below
- inference tools produce wrong rules
- FlyCatcher: authors report 300 of 334 checkers judged correct by cross-validation
- cross-validation is empirical evidence, not a proof of correctness
- avoid treating the remaining 34 as a formally established exhaustive error count
- T2C catches 15 of 20 failures, OmegaGen 20 of 22
- FlyCatcher: authors report 300 of 334 checkers judged correct by cross-validation
- inductive invariant inference is for proofs on models, and âremains a major bottleneckâ (IC3Syn)
- operators fail mostly at the edge with the app
- âare often ad hoc and lack well-defined interfacesâ (Who Watches the Watchers)
- partial failure tools I read are mostly Java based (OmegaGen, Panorama, Dinv is Go)
- follow-up question: which existing Rust tools cover equivalent request-path failures?
research we could do
these are agent proposals
model fidelity and instrumentation experiments are consolidated in research directions, candidate 2
- Anvil uses a Verus model
- TLC replay requires an explicit TLA+ translation or a different checker
- Anvil uses a Verus model
runtime monitor for âeventually stable reconciliationâ
- question: can a monitor detect concrete reconciliation failures with stated environmental assumptions?
- absence of a reviewed live monitor does not establish that none exists
- closest work: Anvil spec, Acto oracles
- first experiment
- run a separate monitor alongside the operator
- read the Kubernetes event stream
- run it on two or three operators from Actoâs bug list and see how many bugs it catches
- distinguish deadline violations from violations of eventual reconciliation
- a finite delay alone does not refute an unbounded eventual property
- account for missed events, API consistency and unstable desired state
- run a separate monitor alongside the operator
agent-written partial failure watchdogs
- gap: OmegaGen needs static analysis built per language
- closest work: OmegaGen, FlyCatcher
- first experiment
- give an agent the ZooKeeper source and a description of a failure from the OmegaGen study
- compare with OmegaGen on the same reproduced cases and collection setup
- a one-case pilot cannot be compared with its aggregate 20-of-22 result
invariants inferred from Jepsen histories and logs
- question: can rules mined from successful chaos tests generalize to unseen workloads and faults?
- novelty requires comparison with distributed invariant inference and test-derived checkers
- closest work: Dinv, T2C
- first experiment
- run Jepsen on etcd or a small Raft clone
- run a Dinv-style miner on the logs
- inject a bug and see which mined rules break
monitor correctness and experimental design
- an inferred rule is a hypothesis until validated beyond the runs that produced it
- training traces can omit legal behavior and make a rule too strict
- missing observations can make a rule too weak
- monitoring a distributed execution requires explicit observation assumptions
- event order, clock uncertainty, loss, duplicate events and crash recovery
- inconsistent snapshots can resemble actual protocol violations
- asynchronous monitoring limits depend on the paperâs specific model
- the language-decidability result assumes distributed monitors with partial information and faults
- it does not imply that offline checking of a complete centrally collected history cannot use real-time order
- candidate research experiment: preserve runtime checks across software changes
- closest work: Oathkeeper, T2C and FlyCatcher
- derive checks from tests before a historical patch
- update them after the patch and evaluate on unseen workloads and known failures
- compare reuse, full regeneration and local repair
- measure false alarms, missed failures, runtime cost and human corrections
- keep separate evaluation sets for legal behavior and faulty behavior
- useful outcome: detect weakening that increases pass rates by losing bug detection
- partial-observation model fidelity experiments are consolidated in research directions, candidate 2
primary-source verification update, 7 Oct 2026 UTC
- opened DCatch PDF and Panorama, OmegaGen and T2C conference abstracts
- OmegaGen abstract confirms 20 detections among 22 reproduced cases
- author quote: âpinpoint the failure scope for 18 casesâ
- opened MongoDB, Cirstea et al., CCF, Multi-Grained, Specula, FlyCatcher and asynchronous-monitoring abstracts
- corrected Oathkeeper link using the OSDI 2022 proceedings page
- the existing snippet-only entries below retain that limitation
could not verify
- FCatch (ASPLOS 2018) primary paper still uninspected
- I4 (SOSP 2019) and SWISS: only seen as a name in another paperâs abstract
- AWS runtime monitoring, CockroachDB/TiKV runtime assertions: no source found
- Dinv, Canopy, Fail-Slow, ShardStore, TaxDC quotes come from search snippets
- Dinv abstract page returned 403
remaining reading gaps
- inspect primary papers for FCatch, I4, SWISS and Dinv
- verify snippet-only deployment claims before reusing them as industry evidence
- distinguish invariant inference for model proofs from inference of live-system assertions
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