🤖 new datacenter hardware, security, clocks, and observability: what breaks and what we could study

new datacenter hardware, security, clocks, and observability: what breaks and what we could study (authored by agents unless marked 🧑)

start here

scope: distributed systems shaped by new hardware, and their security
- shared and remote memory: CXL, RDMA
- programmable network cards
- GPU clusters and their failures
- confidential computing across machines
- clocks, tracing, and diagnosis
my take after reading: the hardware papers mostly chase speed, and the failure story lags behind
- CXL lets hosts share memory, but its specification “does not consider processor failures” (DIKTAMO authors)
- RDMA lets a client write another machine’s memory, so a client that dies halfway leaves a mess nobody owns
- GPUs return wrong numbers without any error, and vendor tests “miss over 60% of defective devices” (ByteDance, OSDI 2026)
- a confidential VM protects running memory, but its disk can be swapped for an old copy at restart
recommendation: pick projects where a correctness or measurement method transfers, since we do not own a hyperscale fleet
- most fleet studies below come from Meta, ByteDance, Alibaba, Microsoft, or Google and cannot be reproduced outside
- what we can do: checkers, fault injectors, proofs, and measurements of public endpoints
every “research we could do” item is an agent hypothesis
- each lists the closest work I found
- none is established as new across the whole literature
reading depth: abstracts and primary landing pages unless a card says otherwise
- quotes are exact words from the linked page
- a quote ending in “…” was cut by me
related slices
- LLM serving covers scheduling and cached state
- networking, peers, and edge covers transport and congestion
- failure and outage studies
- Byzantine consensus

terms

CXL: Compute Express Link, a cable and protocol that lets a CPU use memory outside its own board with ordinary loads and stores
- CXL pod: a few servers wired to the same CXL memory devices
- coherent: every host sees each other’s writes as the CPU caches normally guarantee; today’s devices give this only for a small region
RDMA: remote direct memory access, a network card feature that reads or writes another machine’s memory without running that machine’s CPU
- one-sided: the remote CPU is not involved at all
- RoCE: RDMA carried over Ethernet
disaggregated memory: memory lives on separate machines or devices from the CPUs that use it
SmartNIC, DPU, IPU: a network card with its own CPU cores or programmable logic
- on-path: every packet passes through the programmable part
- off-path: the card’s CPU sits beside the packet path and is used on demand
SDC: silent data corruption, hardware computes a wrong result and reports no error
TEE: trusted execution environment, hardware that hides a program’s memory from the machine’s owner
- confidential VM: a whole virtual machine inside a TEE, e.g. AMD SEV-SNP or Intel TDX
- attestation: a signed statement from the hardware saying which software is running
- rollback attack: the host restarts the program with an older copy of its saved state
clock uncertainty bound: the clock service’s promise “true time is within ±ε of what I report”
trace: the record of one request as it passes through many services
- span: one step of a trace
- head sampling: decide whether to record a request when it enters
- tail sampling: record everything, decide what to keep afterwards
RCA: root cause analysis, finding which component caused an incident

how the pieces relate

memory outside the server
- pool it to save money: Pond, Octopus, Oasis
- share it between hosts as a fast channel: Tigon, MEGALON, Duhu, cMPI, TraCT
- say what happens when one side dies: CXL0, DIKTAMO, Xu et al., rTX, SWARM
- prove programs on it correct: LOCO, Abdulla et al.
network cards that compute
- measure what they are good at: Wei et al., dpBento
- move work onto them: Wave, PD3, μView, FORGE
- open the transport for change: SCR, BALBOA, UCCL-Tran
- put trust in them: TNIC, Recipe
GPU clusters
- how often they fail: Kokolis et al., Cui et al., Hu et al., Kang et al.
- find the bad machine: Minder, Holmes, Aegis, FLARE, SCOUT
- catch wrong numbers: SDCHunter, AEGIS (OSDI 2026), Ma et al., TrainCheck
- recover faster: Leto, PHOENIX, FlashRecovery
confidential computing
- physical and software attacks on the hardware: TEE.fail, Battering RAM, WireTap, Heckler
- stale state at restart: Rollbaccine, Keshavarzi et al., Chimera, Rebound, CRISP
- proving who you talk to: attested DNS, attestation in TLS, cross-TEE attestation
- cost on GPUs: Yin and Wang
time and diagnosis
- keep clocks close: Sundial, Graham, Firefly, SyncWise
- use close clocks: Tiga, K2
- record requests cheaply: Hindsight, Mint, Trace Sampling 2.0, StriaTrace
- repair broken request records: Backstitch
- let LLM agents diagnose: AIOpsLab, PRAXIS, Kim et al.

source cards: memory outside the server (CXL)

Pond, Li et al., ASPLOS 2023
- arXiv abstract
- exact words: “pooling across 8-16 sockets is enough to achieve most of the benefits”
- author result: “reduces DRAM costs by 7% with performance within 1-5% of same-NUMA-node VM allocations”
- why it matters: this is the economic case that made cloud providers build small CXL pools
Octopus, Zhong et al., NSDI 2026
- arXiv abstract
- idea: wire each server to a few small pooling devices and skip the switch
- author result: “Octopus RPCs are 3.2x faster than in-rack RDMA and 2.4x faster than CXL switches”
- author result from simulation: “net server cost savings of 3-5.4% whereas CXL switches result in a net cost increase”
- scope: “a three-server CXL pod prototype”, larger pods are simulated
Oasis, Zhong et al., SOSP 2025, arXiv title “My CXL Pool Obviates Your PCIe Switch”
- arXiv abstract
- exact words: “PCIe device pooling can be effectively implemented in software using CXL memory pools”
- exact words: “CXL memory pools improve memory utilization and already have positive return on investment”
- inference: once a pod shares memory, it also shares network cards and disks, so one host’s failure touches devices other hosts use
Tigon, Huang et al., OSDI 2025
- USENIX page
- exact words: “the first distributed in-memory database that synchronizes cross-host concurrent data accesses using atomic operations on CXL memory”
- limits the authors name: “CXL’s higher latency and lower bandwidth relative to local DRAM, and its limited hardware support for cross-host cache coherence”
- author result: “up to 18.5× higher throughput compared with an RDMA-based distributed database”
MEGALON, Hu et al., OSDI 2026
- USENIX page
- exact words: “the hardware is expected to provide cache coherence only for a small region of CXL memory and it is difficult for hosts to share data in the non-coherent region”
- idea: replicate big, rarely updated bookkeeping; keep only small, hot bookkeeping in the coherent region
Duhu, Men et al., OSDI 2026
- USENIX page
- exact words: “current SDM clusters provide weak coherence guarantees”
- idea: an object store on shared memory so Ray needs no changes
- author result: “improve job completion time (JCT) by up to 3.39× on a shuffle workload”
MemChannel, Guo et al., NSDI 2026
- USENIX page
- the authors measured a real CXL switch and “identify three issues: intra-host contention, in-fabric congestion, and unmanaged host-remote DIMM interaction”
- inference: a CXL fabric now needs congestion control, like a network
DRack, Zhang et al., USENIX ATC 2025
- USENIX page
- exact words: “DRack disaggregates all NICs within a rack from their hosts, forming a shared NIC pool”
cMPI, Wang et al., 2025 preprint
- arXiv abstract
- exact words: “transforming cross-node communication into memory transactions and data copies within CXL memory”
TraCT, Yoon et al., 2025 preprint
- arXiv abstract
- claim: in LLM serving that splits input processing from output generation, “KV transfer dominates both time-to-first-token (TTFT) and peak throughput”
- idea: pass the cached model state through CXL shared memory instead of RDMA
NEMO, Li et al., OSDI 2026
- USENIX page
- a memory usage counter engine inside the memory controller, prototyped “on an FPGA-based CXL-attached memory expander”
failure models for CXL shared memory
- Xu et al., 2024 preprint, “CXL Shared Memory Programming: Barely Distributed and Almost Persistent”
  - arXiv abstract
  - exact words: “processes can fail before data does, or data might fail before a process does”
  - exact words: “The lack of a failure model for CXL-based shared memory makes it challenging to understand and mitigate these failures.”
- CXL0, Assa et al., ASPLOS 2026
  - arXiv abstract
  - exact words: “CXL currently lacks an adequate programming model, making it impossible to reason about the correctness and behavior of systems on top”
  - the authors call CXL0 “the first programming model for concurrent programs over CXL”
  - they give “a general transformation that enhances any linearizable concurrent algorithm with durability in a distributed partial-crash setting”
    - linearizable: every operation appears to happen at one instant
    - partial crash: some hosts die while the shared memory and other hosts live on
- DIKTAMO, Psistakis et al., MICRO 2026
  - arXiv abstract
  - exact words: “a node failure leads to the loss of the dirty data in its caches, corrupting application state”
  - exact words: “Sadly, the CXL specification does not consider processor failures.”
- PMRobust, Guo et al., 2025 preprint
  - arXiv abstract
  - exact words: “No existing tools can ensure the absence of missing flush bugs.”
  - idea: a compiler inserts the flush instructions
- Hadi et al., 2026 preprint
  - arXiv abstract
  - claim: making data durable across a CXL fabric is slow because “persist operations must traverse the entire CXL fabric”
deployment status
- unverified secondary claim: a vendor blog says Microsoft launched CXL cloud instances in November 2025
  - Introl blog
  - I did not find Microsoft’s own announcement

source cards: RDMA and memory on other machines

Empowering Azure Storage with RDMA, Bai et al., NSDI 2023
- USENIX page
- exact words: “Today, around 70% of traffic in Azure is RDMA and intra-region RDMA is supported in all Azure public regions.”
- named challenge: “the problem of interoperability between different types of RDMA network interface cards”
Mitigating Scalability Walls of RDMA-based Container Networks, Liu et al., NSDI 2025
- USENIX page
- exact words: “most performance issues are related to RDMA NICs (RNICs), whose design and implementation defects might constitute the “scalability wall”“
- exact words: “we are challenged by the limited visibility into the internals of today’s RNICs”
- inference: operators debug the card as a black box, by experiment
White-Boxing RDMA, Zhao et al., NSDI 2025
- USENIX page
- exact words: “RDMA’s hardware-offloading nature poses significant rigidity when landing these innovations”
- idea: software decides per packet, hardware still moves the data
RoCE BALBOA, Heer et al., OSDI 2026
- USENIX page
- exact words: “an open-source, 100 Gbps RDMA offload engine designed for research on networking and fully compatible with commercial RNICs”
- use for us: an RDMA stack whose insides can be read and changed
UCCL-Tran, Zhou et al., OSDI 2026
- USENIX page
- exact words: “single-path RDMA traffic is prone to flow collisions that severely degrade collective communication performance”
- author result: “up to 4.5× higher performance compared to existing RDMA NICs”
MRC, Araujo et al., 2026 preprint
- arXiv abstract
- exact words: “a new RDMA-based transport protocol, MRC, sprays across many paths and actively load-balances between them”
- target scale: “training clusters well over 100K GPUs”
Ethereal, Addanki et al., 2024 preprint
- arXiv abstract
- asks “How close can singlepath transport come to” packet spraying, against the common belief that spraying is necessary
Celeris, 2025 preprint, “Reimagining RDMA Through the Lens of ML”
- arXiv abstract
- exact words: “removes retransmissions and in-order delivery from the RDMA NIC”
- reason given: training tolerates some lost data
- status: “Early results”
Varuna, Wang et al., 2026 preprint
- arXiv abstract
- exact words: “upon failure, uniformly retransmit all in-flight RDMA request over the backup path”
- exact words: “Retransmitting post-failure requests is not only redundant (consuming bandwidth), but also incorrect for non-idempotent operations, where duplicate execution can violate application semantics.”
- idea: attach a small completion log to every operation, so after a link failure the sender learns which requests ran
- inference: this is the same question as the shortlist’s “did my remote update happen?”, one layer down
systems on RDMA memory nodes
- FUSEE, Shen et al., FAST 2023
  - arXiv abstract
  - clients manage the index themselves, and the system “handles complex failures under the DM architecture”
- rTX, Wei et al., 2023 preprint
  - arXiv abstract
  - exact words: “Current indexes focus on performance improvements and largely ignore tolerating client failures.”
- SWARM, Murat et al., SOSP 2024
  - arXiv abstract
  - claims “single-roundtrip reads and writes in the common case”, “strong consistency (linearizability)”, and “strong liveness (wait-freedom)”
- uBFT, Aguilera et al., ASPLOS 2023
  - arXiv abstract
  - exact words: “pure crashes appear to be a mere illusion with real-life systems reportedly failing in many unexpected ways”
  - idea: use remote memory as a small trusted part to tolerate arbitrary faults with fewer replicas
- Lotus, Hu et al., 2025 preprint
  - arXiv abstract
  - exact words: “the RDMA network interface cards at MNs become a primary performance bottleneck”
- HDTX, Lu et al., USENIX ATC 2025
  - USENIX page
  - a faster commit protocol; compared against FaRM and FORD
- FARLock, Hu et al., OSDI 2026
  - USENIX page
  - exact words: “they fail to grant locks in the expected first-come first-serve manner”
- OneSidedMW, Wang et al., NSDI 2026
  - USENIX page
  - names “security vulnerabilities” among the problems of current remote memory management
  - idea: the card itself grants and revokes access to small memory ranges
- FORGE, Yang et al., OSDI 2026
  - USENIX page
  - a cache on remote memory that syncs groups of objects, not single ones
proofs about RDMA programs
- LOCO, Ambal et al., 2025 preprint
  - arXiv abstract
  - exact words: “baseline RDMA comprises a highly permissive weak memory model that is difficult to use in practice and has only recently been formalised”
  - a verified library of objects that span machines
- Abdulla et al., CAV 2026
  - arXiv abstract
  - exact words: “We show that reachability is undecidable in general, even for a restricted fragment of the model.”
  - plain meaning: no tool can always decide whether an RDMA program can reach a bad state
security of RDMA
- Kornfeld Simpson et al., HotCloud 2020
  - USENIX page
  - named problems: “changes in RPC reliability guarantees and unauditable data-accesses”
far memory used as swap
- PD3, Sankhe et al., NSDI 2026: see the network card section
- Redy, Zhang et al., VLDB 2022
  - arXiv abstract
  - a cache service on leftover memory and spot VMs that “handles the dynamics of remote memory regions”

source cards: network cards that compute

Characterizing Off-path SmartNIC, Wei et al., OSDI 2023
- arXiv abstract
- exact words: “the first holistic study of a representative off-path SmartNIC, specifically the Bluefield-2, from a communication-path perspective”
dpBento, Hu et al., 2025 preprint
- arXiv abstract
- exact words: “a comprehensive view of the implications of DPUs for data processing is missing”
Wave, Humphries et al., ASPLOS 2025
- arXiv abstract
- moves operating system decisions, such as scheduling, to the card’s ARM cores
- reason given: “virtually all server resources are available to paying customers”
OSMOSIS, Khalilov et al., 2023 preprint
- arXiv abstract
- exact words: “existing on-path SmartNICs have resource multiplexing limitations”
PD3, Sankhe et al., NSDI 2026
- USENIX page
- the card reads the request first and fetches remote memory before the server needs it
μView, Cornacchia et al., NSDI 2026
- USENIX page
- title claim: “Observability Is Eating Your Cores”
- idea: summarize service metrics on the card, close to the service
ZOC, Guan et al., NSDI 2026
- USENIX page
- a cloud provider replaces the special card with a service VM on ordinary servers
- reasons given: “operational inconsistency across heterogeneous fleets, limited resource elasticity, and performance bottlenecks caused by slow-path processing”
- inference: even a large cloud finds special cards costly to operate
SCENIC, Ramhorst et al., 2026 preprint
- arXiv abstract
- exact words: “Commercial SmartNICs provide high bandwidth and easy software integration, but offer limited support for customization and data processing offload.”
TNIC, Giantsidi et al., 2025 preprint
- arXiv abstract
- exact words: “a minimal, formally verified, silicon root-of-trust at the network interface level”
- two properties it gives: “transferable authentication and non-equivocation”
  - non-equivocation: a machine cannot tell two peers different things
Recipe, Giantsidi et al., 2025 preprint
- arXiv abstract
- exact words: “Traditional Crash Fault Tolerant (CFT) protocols, which assume a fail-stop model, are inadequate for untrusted cloud environments”
- idea: turn crash-tolerant protocols into ones that survive lying machines, using trusted hardware

source cards: GPU clusters as distributed systems

how often things fail
- Kokolis et al., Meta, HPCA 2025, “Revisiting Reliability in Large-Scale Machine Learning Research Clusters”
  - arXiv abstract
  - exact words: “while large jobs are most vulnerable to failures, smaller jobs make up the majority of jobs in the clusters”
- Cui et al., 2025 preprint, “Story of Two GPUs”
  - arXiv abstract
  - data: “2.5 years of operational data (11.7 million GPU hours)” on 1,056 GPUs
  - exact words: “H100 GPU memory resilience is worse than A100 GPU memory, with 3.2x lower per-GPU MTBE for memory errors”
    - MTBE: mean time between errors
- Hu et al., NSDI 2024, “Characterization of Large Language Model Development in the Datacenter”
  - arXiv abstract
  - six months of traces; names “frequent hardware failures, intricate parallelization strategies, and imbalanced resource utilization”
- Kang et al., Lablup technical report, 2026
  - arXiv abstract
  - exact words: “hardware failures are routine operating conditions rather than rare exceptions, yet public operational evidence from production training clusters remains limited”
  - found “a 60-node-scale storage I/O bottleneck absent in 2-4-node tests”
  - scope: 63 nodes, 504 GPUs, 55 days of metrics
- Ma et al., KDD 2026, “Don’t Predict, Prioritize”
  - arXiv abstract
  - claim: predicting the exact time of a GPU failure “is inherently difficult”
- Sun et al., ByteDance, 2026 preprint
  - arXiv abstract
  - three obstacles: “workload-confounded telemetry, heterogeneous fault precursors, and the gap between window-level predictions and actionable alerts”
find the bad machine
- Minder, Deng et al., NSDI 2025
  - arXiv abstract
  - exact words: “a training task can encounter two faults per day on average, possibly leading to a halt for hours”
  - author result: reacts “within 3.6 seconds on average, with a precision of 0.904 and F1-score of 0.893”
- Holmes, Yao et al., NSDI 2025
  - USENIX page
  - exact words: “some irregular iterations taking even more than twice the time of a normal iteration”
  - exact words: “which is even more severe than the impact of failures”
  - plain meaning: slow steps cost more training time than crashes do
- Aegis, Dong et al., NSDI 2025
  - USENIX page
  - the second version “chose to customize the collective communication library for sophisticated failure localization in runtime without modifying customer code”
- FLARE, Cui et al., NSDI 2026
  - USENIX page
  - exact words: “existing diagnostic tools are narrowly tailored to specific issues”
  - deployed “across 6,000 GPUs”
- SCOUT, Wang, 2026 preprint
  - arXiv abstract
  - exact words: “identify outliers through strict-majority consensus among equivalent replicas”
  - plain meaning: GPUs doing the same job should agree, so the odd one out is the suspect
- VCCL, Zhang et al., 2025 preprint
  - arXiv abstract
  - names “expensive restart costs under link failures” and “insufficient observability of transient collective communication anomalies” in NVIDIA’s communication library
- PrismLLM, Xi et al., 2026 preprint
  - arXiv abstract
  - exact words: “engineers often need to reproduce production behaviors to diagnose failures or evaluate optimizations”
  - idea: emulate a large training run on a few GPUs
wrong numbers without an error
- Dixit et al., Meta, 2021, “Silent Data Corruptions at Scale”
  - arXiv abstract
  - exact words: “This has resulted in hundreds of CPUs detected for these errors, showing that SDCs are a systemic issue across generations.”
- SDCs in the Wild, Zheng et al., ByteDance, OSDI 2026
  - USENIX page
  - exact words: “our experience shows these methods miss over 60% of defective devices”
  - exact words: “SDCs are highly data-dependent and unit-specific, meaning devices that pass general stress tests often fail under specific training input data”
  - exact words: “standard ECC and thermal protections fail to capture these logic-level bit flips”
  - method: replay the exact training step and input that failed
  - scope: 23 defective GPUs
- AEGIS, Lei et al., OSDI 2026, “Safeguarding LLM Training at Scale”
  - USENIX page
  - author result: over 35 million GPU hours it “identified 18 real-world SDC incidents and 13 faulty GPUs while incurring only 0.86% performance overhead”
  - not the same system as the NSDI 2025 Aegis above
- Ma et al., 2025 preprint, “Understanding Silent Data Corruption in LLM Training”
  - arXiv abstract
  - method: “comparing model training between healthy production nodes and unhealthy nodes exhibiting SDCs”
- Tung et al., DSN 2026 industry track
  - arXiv abstract
  - from simulated hardware faults: “NaN/+INF/-INF account for only 1.01% of SDC outcomes”
  - plain meaning: checking for NaN catches almost none of them
- LLM-PRISM, Tyagi et al., 2026
  - arXiv abstract
  - exact words: “while LLMs resist low-frequency faults, impact is highly non-uniform”
- TrainSDC, Xia et al., 2026 preprint
  - arXiv abstract
  - exact words: “faults on the Q/K path producing persistent training deviations”
- TrainCheck, Jiang et al., OSDI 2025
  - USENIX page
  - software-caused silent errors, not hardware: it “automatically infers invariants tailored for DL training”
  - author result: “detects 18 errors within a single training iteration” out of 20 reproduced
- SAVE, Zheng et al., USENIX ATC 2025
  - USENIX page
  - protects model inference from GPU memory bit flips; aimed at “safety-critical edge applications”
recover faster
- Leto, Kim et al., 2026 preprint
  - arXiv abstract
  - exact words: “Existing recovery systems nevertheless reload checkpoints, recompute lost progress, and rebuild process state, idling GPUs that could otherwise continue training.”
- PHOENIX, Xie et al., 2026 preprint
  - arXiv abstract
  - swaps a failed node for a spare while training continues
- FlashRecovery, Zhang et al., 2025 preprint
  - arXiv abstract
slow hardware in general
- Sieve, Dong et al., USENIX ATC 2025
  - USENIX page
  - studied “48 real-world fail-slow hardware failures”
  - finding: slow hardware breaks “synchronized and timeout mechanisms” in the software above it
- FiDe, Rovelli et al., USENIX ATC 2025
  - USENIX page
  - claims to “report the crash of a remote process in a datacenter within less than 30 μs”
tracing for LLM serving
- StriaTrace, Wu et al., OSDI 2026
  - USENIX page
  - exact words: “existing tracing tools incur prohibitive overhead”
  - three rules from production: “(1) tracing key synchronization points, (2) tracing critical paths, and (3) detailed tracing only during abnormalities”
  - author result: “diagnosed hundreds of abnormalities spanning 19 distinct root causes”

source cards: confidential computing across machines

stale state at restart
- Rollbaccine, Chu et al., SIGMOD 2026
  - arXiv abstract
  - exact words: “TEEs do not protect applications against disk rollback attacks, where persistent storage can be reverted to an earlier state after a crash”
  - idea: a disk layer that replicates writes so any unmodified application is protected
- Keshavarzi, Chockler, Gotsman, 2025, “TEE is not a Healer”
  - arXiv abstract
  - exact words: “the protection offered by a TEE only applies during program execution”
- Chimera, Liu et al., 2026 preprint
  - arXiv abstract
  - exact words: “during recovery, a compromised host can roll back a crashed enclave to a stale persistent state”
  - trade-off named: defenses “either impose substantial overhead on critical consensus paths” or “incur prolonged recovery delays”
- Rebound, 2025 preprint, “It’s a Feature, Not a Bug”
  - arXiv abstract
  - exact words: “they categorically treat all rollback as malicious and thus preclude legitimate rollbacks used for operational recovery from corruption or misconfiguration”
  - plain meaning: operators also roll back on purpose, so the defense must tell the two apart
- CRISP, 2024
  - arXiv abstract
  - exact words: “During restarts, attackers can revert the state of confidential services to a previous version”
  - context: Kubernetes restarts services often, so the window opens often
consensus with trusted hardware
- Wen et al., EuroSys 2027, “Breaking Fault Lines”
  - arXiv abstract
  - setting: only some replicas have a TEE
  - exact words: “TEEs improve fault tolerance only once they exceed two-thirds of the deployment”
- Smart Casual Verification of the Confidential Consortium Framework, Howard et al., NSDI 2025
  - USENIX page
  - method: “binding the formal specification in TLA+ to the C++ implementation”
  - author result: “find six subtle bugs in the design and implementation before they could impact production”
proving who you talk to
- attested DNS, Delignat-Lavaud et al., 2025 preprint
  - arXiv abstract
  - problem: attestation “requires custom clients and protocols to distribute, update, and verify their attestation evidence”
  - idea: bind the attested software to a domain name so ordinary clients benefit
- Weinhold et al., USENIX ATC 2025, “Separate but Together”
  - USENIX page
  - exact words: “setting up a secure channel to such a TEE requires a security guarantee that the channel actually terminates inside the TEE”
  - on earlier ways to put attestation into TLS: “Unfortunately, they all have shortcomings.”
- Andrade et al., 2026 preprint, “Know Thy Neighbor”
  - arXiv abstract
  - two services on different TEE types must each check the other
attacks on the hardware promise
- TEE.fail, 2025
  - project page
  - exact words: “extract cryptographic keys from Intel TDX and AMD SEV-SNP with Ciphertext Hiding, including in some cases secret attestation keys from fully updated machines in trusted status”
  - exact words: “extracted attestation keys can be used to compromise Nvidia’s GPU Confidential Computing, allowing attackers to run AI workloads without any TEE protections”
  - method: a device on the memory wires built “using only off the shelf electronic equipment”
  - plain meaning: someone with hands on the server can forge “I am a genuine protected machine”
  - reading depth: the project page’s summary, not the paper
- WireTap, 2025
  - project page
  - exact words: “we are able to extract an SGX secret attestation key from a machine in fully trusted status”
  - the authors then attack “SGX-backed blockchain deployments”
- Battering RAM, 2025
  - project page
  - De Meulemeester et al., IEEE S&P 2026
  - exact words: “a simple, $50 interposer that sits quietly in the memory path, behaving transparently during startup and passing all trust checks”
  - exact words: “silently redirects protected addresses to attacker-controlled locations, allowing corruption or replay of encrypted memory”
  - the same page announces a follow-up: “DDRop (CCS ’26): dropping DDR5 writes breaks TDX and SEV-SNP!”
- Heckler and WeSee, Schlüter et al., 2024
  - Heckler abstract
  - WeSee abstract
  - a malicious host injects interrupts into a confidential VM
- Shen and Qin, 2026 preprint
  - arXiv abstract
  - on one AMD server generation: “this protection is insufficient on EPYC Milan by presenting a software-only exploit”
  - what leaks: the root seed from which attestation signing keys are derived
- Google and Intel, 2026 white paper on TDX live migration
  - arXiv abstract
  - reviewed “support for Live Migration and Trusted Domain (TD) Partitioning”
  - inference: moving a confidential VM between machines is new attack surface that vendors themselves audit
confidential serverless, databases, and GPUs
- Wallet, Sabanic et al., NSDI 2026
  - USENIX page
  - author result: “4.3× smaller TCB” than a confidential VM per function
    - TCB: trusted computing base, the code you must trust
- ZENO, Huang et al., OSDI 2026
  - USENIX page
  - removes encryption work from the query path; “integrated into GaussDB”
- Yin and Wang, 2026 preprint, “The Serialized Bridge”
  - arXiv abstract
  - exact words: “LLM serving under Intel TDX plus GPU-CC still loses 13-27% of throughput, and KV-cache restore latency can more than double”
  - cause named: “the confidential VM-GPU bridge, not GPU compute”
- Chrapek et al., 2025 preprint
  - arXiv abstract
  - runs Llama2 inference fully inside CPU and GPU TEEs and reports cost
supply chain
- Meiklejohn et al., Google, 2025
  - arXiv abstract
  - exact words: “the current ecosystem for open ML models contains significant supply-chain risks, some of which have been exploited already in real attacks”
- SBOMproof, Bufalino et al., 2025 preprint
  - arXiv abstract
  - checks whether software ingredient lists for container images are accurate
- Przymus and Durieux, 2025, on the XZ Utils backdoor
  - arXiv abstract

source cards: clocks

Sundial, Li et al., Google, OSDI 2020
- USENIX page
- exact words: “achieves ~100ns time-uncertainty bound under various types of failures”
- exact words: “in large-scale datacenters, temperature-related, link, device, and domain failures are common”
Graham, Najafi and Wei, NSDI 2022
- USENIX page
- idea: learn how the local clock drifts from sensors every server has
- author result: “reducing the maximum assumed drift in most situations from 200ppm to 100ppb”
Firefly, Google, SIGCOMM 2025
- Google Cloud blog
- exact words from the blog: “regulatory requirements mandate sub-100µs external synchronization to Coordinated Universal Time, or UTC, and fairness demands sub-10ns internal clock synchronization”
- exact words: “doing so on cloud-hosted infrastructure has traditionally been impossible”
- a search summary of the paper, not checked word for word: under 10 ns between devices in a 248-machine network
SyncWise, Lei et al., NSDI 2026
- USENIX page
- clocks for networks whose optical links are rewired every few microseconds
- exact words: “the first protocol to attain sub-10 ns maximum sync error”, in simulation
bittide, Bastiaan et al., 2025 preprint
- arXiv abstract
- exact words: “the first hardware implementation of bittide, a decentralized clock synchronization mechanism for achieving logical synchrony”
- scope: 8 FPGA boards
systems that rely on close clocks
- Tiga, Geng et al., SOSP 2025
  - arXiv abstract
  - exact words: “uses synchronized clocks to proactively order transactions by assigning each a future timestamp at submission”
- K2, Song et al., VLDB 2025
  - arXiv abstract
  - exact words: “TrueTime clocks (TTCs) that offer accurate and reliable time within limited uncertainty bounds have been increasingly implemented in many clouds”
- Nezha, Geng et al., VLDB 2023
  - arXiv abstract
  - consensus that orders requests by synchronized clocks
clocks and traces
- TempoTrace, Elbakoury and Sharma, 2026 preprint
  - arXiv abstract
  - exact words: “Distributed tracing in large-scale AI infrastructure fails silently when clock accuracy is insufficient”
  - caution: a 43-page preprint with strong numeric claims; I have not checked them

source cards: tracing and diagnosis

record requests cheaply
- Hindsight, Zhang et al., NSDI 2023
  - USENIX page
  - exact words: “Hindsight lazily retrieves trace data only after symptoms of a problem are detected”
  - the authors’ picture: “a car dash-cam that, upon detecting a sudden jolt in momentum, persists the last hour of footage”
- Mint, Huang et al., ASPLOS 2025
  - arXiv abstract
  - idea: store the common shape of traces once, keep the differing values
  - author result: “trace storage (reduced to an average of 2.7%) and network overhead (reduced to an average of 4.2%)”
- Trace Sampling 2.0, Wu et al., 2025 preprint
  - arXiv abstract
  - samples single steps while “maintaining trace structure consistency”
- DiTing, Ren et al., OSDI 2026
  - USENIX page
  - exact words: “telemetry data are often stored and processed in siloed systems”
  - one store for logs, metrics, and traces on spare cloud capacity
broken request records
- Backstitch, 2026 preprint
  - arXiv abstract
  - exact words: “At handoffs outside instrumented paths, e.g., custom queues and callbacks, the payload continues but the context does not”
  - exact words: “673 of 1,133 services carried at least one”
  - an LLM agent finds and repairs these breaks at “a major video platform”
compare and reuse traces
- Contrast, 2026 preprint
  - arXiv abstract
  - exact words: “Diagnosis using distributed traces is fundamentally a comparative task”
- Palette, Anand et al., 2025 preprint
  - arXiv abstract
  - exact words: “researchers and practitioners alike often do not have access to representative systems”
  - builds runnable test systems from public company traces
LLM agents that diagnose
- AIOpsLab, Chen et al., 2025
  - arXiv abstract
  - a test bed that “deploys microservice cloud environments, injects faults, generates workloads, and exports telemetry data”
- Kim et al., 2026 preprint, “Why Do AI Agents Systematically Fail at Cloud Root Cause Analysis?”
  - arXiv abstract
  - exact words: “existing systems exhibit low detection accuracy even with capable models”
  - method: “1,675 agent runs” classified “into 12 pitfall types”
- PRAXIS, Cui et al., DSN 2026
  - arXiv abstract
  - the agent walks a service graph and a code dependency graph
  - author result: “improves RCA accuracy by up to 6.3x while reducing token consumption by 5.3x”
- UModel, Pei et al., 2026 preprint
  - arXiv abstract
  - exact words: “fragmented data silos, incompatible schemas, and insufficient semantic metadata”
- Riddell et al., FORGE 2026, “Stalled, Biased, and Confused”
  - arXiv abstract
  - studies how LLM reasoning fails when “symptoms appear far from their true causes”

research we could do

1. test what a confidential service does when its disk goes back in time
- question: which open-source services, moved unchanged into a confidential VM, misbehave when restarted on an old disk copy?
- why I think it fits: this is fault injection, which we know, pointed at a fault the cloud operator controls
- existing work
  - Rollbaccine, CRISP, Chimera, and Keshavarzi et al. build defenses
  - Rebound shows operators also need honest rollback
  - CCF’s authors model-check their own protocol
  - I found no tool that injects rollback into arbitrary services the way Jepsen injects partitions; I have not searched security venues fully
- first week
  - run etcd, a key manager, and one database in confidential VMs
  - restart one replica on a disk snapshot from minutes earlier
  - record what an outside client can observe: lost acknowledged writes, reused nonces, reissued tokens, two leaders
- independent check: a client-side log of acknowledged operations, compared with later reads
- rejection condition: the tested rollback patterns produce no client-visible violation under the stated replication and recovery assumptions
  - a finite test cannot show that replication hides every rollback
  - separate a stale replica rejoining from coordinated rollback and replay of an old complete deployment
1. measure whether deployed “confidential” services can actually be checked by a client
- question: of public services that advertise TEE protection, how many give a client attestation evidence, and what does that evidence prove?
- why I think it fits: it is web measurement applied to a security promise
- existing work
  - attested DNS and Weinhold et al. say today’s verification needs custom clients
  - TEE.fail, WireTap, and Shen and Qin show attestation keys can leak from machines in good standing
  - Yin and Wang, Chrapek et al. measure cost, not verifiability
- first week
  - list confidential inference, key management, and messaging services with public endpoints
  - fetch their attestation evidence and record: hardware type, firmware version, whether the measured software is published and reproducible, how fresh the evidence is
  - check whether any verifier rejects hardware generations with known key extraction
- independent check: a second person rebuilds one service’s published image and compares the measurement
- risk: the population may be a few dozen services; then it is a case study, not a measurement
1. a failure contract for memory shared over CXL, checked in Rust
- question: can a small Rust library give “one host died with unwritten cache lines” a checked recovery rule?
- existing work
  - CXL0 defines a formal model and a transformation for durable linearizable code
  - DIKTAMO changes the hardware
  - LOCO verifies objects over RDMA, not CXL
  - Tigon, MEGALON, and Duhu write their own sharing protocols by hand
  - the storage slice covers verified crash consistency for persistent memory
    - verification boundaries
- first week
  - write CXL0’s rules as a tiny executable model
  - model-check a shared log and a lock under “host loses cache” faults
  - try the same faults on Tigon’s open-source code with shared memory emulation
- what would make it a contribution: a bug in a published system, or a verified primitive those systems could adopt
- stop if CXL0’s transformation already covers the partly coherent setting MEGALON and Duhu target
1. keep request identity across async Rust boundaries
- question: how often do Rust services lose the request’s trace context at spawns, channels, and queues?
- why I think it fits: Backstitch reports the break in 673 of 1,133 services at one company, in languages it does not name
- existing work
  - Backstitch repairs breaks with an agent on a private fleet
  - Hindsight and Mint assume context arrives intact
  - the Rust slice studies cancellation across boundaries
    - Rust distributed systems
- first week
  - pick twenty open-source Rust services that use the tracing or OpenTelemetry crates
  - tag requests at entry and count where the tag is missing downstream
  - classify breaks: detached task, channel hop, thread pool, external queue
- possible outcome: a lint or a type-level rule, plus the first public count
- stop if breaks are rare in Rust because the common libraries already carry context
1. a public test bed for wrong-number detectors in multi-GPU jobs
- question: do the published detection ideas still work outside the fleet they were tuned on?
- existing work
  - SDCHunter and AEGIS report fleet results nobody else can rerun
  - SCOUT compares replicas; TrainSDC, LLM-PRISM, and Tung et al. inject simulated faults
  - Tung et al. report that NaN checks see about 1% of corruptions
  - PrismLLM emulates large runs on few GPUs
- first week
  - inject bit patterns from Tung et al.’s published statistics into a small multi-GPU run
  - compare three cheap checks: NaN check, replica agreement, checksum on matrix multiply
  - report detection rate and overhead
- gap I am less sure of: serving, where no gradient averages the error away and one wrong token reaches a user
  - SAVE targets edge devices; I found no fleet study for LLM serving
- risk: without real defective GPUs the injected faults may not look like real ones; ByteDance says real ones depend on the exact input
1. check the clock’s promise from the outside
- question: when a cloud clock service says “within ±ε”, how often is that false, and do systems built on it notice?
- existing work
  - Sundial and Graham design for failures; Firefly and SyncWise push accuracy
  - Tiga, K2, and Nezha assume the bound holds
  - TempoTrace claims trace ordering breaks under loose clocks, unverified
- first week
  - first identify what each bound promises: relative agreement, UTC accuracy, or both
  - compare reported intervals across VM pairs while measuring network delay
    - inconsistent intervals can refute joint promises under explicit delay assumptions
    - agreement alone cannot establish UTC accuracy because both clocks can share the same error
  - use an independent time reference with a stated error bound before claiming absolute accuracy
  - inject a bound violation into one clock-ordered open-source system and check its behavior
    - this controlled experiment does not measure the frequency of real cloud violations
- weak point: I have not yet searched for existing cloud clock measurements, so this may be done
weaker ideas, listed so nobody repeats the thinking
- another LLM diagnosis agent: crowded; AIOpsLab, PRAXIS, UModel, and Kim et al. already cover building and criticizing them
- another trace sampler: Hindsight, Mint, and Trace Sampling 2.0 cover the main design choices
- anything needing a hyperscale GPU fleet or custom CXL hardware

Last edited: 2026-10-08

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Steven Hé (Sīchàng)