Paper Citation Record · LEDGER

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML

As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.22702.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.22702 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:25:55.225075Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact3
verified fuzzy4
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch9

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6471ebd-a90c-4a7a-95d4-63a5bd4fb380 · outbound

This paper cites Efficient privacy preserving data collection and computation offloading for fog-assisted iot,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Efficient privacy preserving data collection and computation offloading for fog-assisted iot,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.087474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a165e4db-6d82-4f29-a4e8-4cb554b09ad9 · outbound

This paper cites Revisiting edge ai: Opportunities and challenges,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Revisiting edge ai: Opportunities and challenges,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:25:56.983205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d3f23a8e-6dc1-4a3d-8669-ab762cdd3c2d · outbound

This paper cites Machine learning in real-time internet of things (iot) systems: A survey,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Machine learning in real-time internet of things (iot) systems: A survey,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.097298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fb6242d-d2f8-423c-a956-4253bedbc750 · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.102349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55e4504-1694-4c52-99c2-55f579716974 · outbound

This paper cites Compensated-dnn: energy efficient low-precision deep neural networks by compensating quantization errors,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Compensated-dnn: energy efficient low-precision deep neural networks by compensating quantization errors,

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:56.734047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c9975c8-589e-4fef-b868-4d21ad1f241a · outbound

This paper cites Conditional deep learning for energy-efficient and enhanced pattern recognition,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Conditional deep learning for energy-efficient and enhanced pattern recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:25:56.966122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f5bc520-e10b-49cb-996e-5654fb04828e · outbound

This paper cites A survey of faults and fault- injection techniques in edge computing systems,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML A survey of faults and fault- injection techniques in edge computing systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.118865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed77d2ff-ab3f-4231-a2b0-8881c5ca1357 · outbound

This paper cites A survey of autoscaling in kubernetes,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML A survey of autoscaling in kubernetes,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.123944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c081f5a-f5a3-4a87-b120-31350ec8de2f · outbound

This paper cites A survey of kubernetes scheduling algorithms,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML A survey of kubernetes scheduling algorithms,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.129505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5f4de6a-7806-4100-9557-dd6c9aca956b · outbound

This paper cites Custom scheduling in kubernetes: A survey on common problems and solution approaches,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Custom scheduling in kubernetes: A survey on common problems and solution approaches,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.134559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbbe2d1e-6e95-4467-b981-f5da392bc398 · outbound

This paper cites Investigating quality attributes of machine learning inference on the edge-cloud continuum,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Investigating quality attributes of machine learning inference on the edge-cloud continuum,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:25:56.949491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be72a642-2e60-4d4f-be86-a020d1a08516 · outbound

This paper cites Frisbee: A suite for benchmarking systems recovery,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Frisbee: A suite for benchmarking systems recovery,

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:56.494964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 612f4b04-58e2-4f3d-8a6f-8adae14545ee · outbound

This paper cites Kalka and T.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Kalka and T

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T11:25:55.299455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5301661-a645-47fa-aa59-f8b8914b37d2 · outbound

This paper cites Edgecloudsim: An environment for performance evaluation of edge computing systems,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Edgecloudsim: An environment for performance evaluation of edge computing systems,

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:56.399147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a089530a-70be-471e-91fc-1eba9d43e4c2 · outbound

This paper cites Cloud continuum: The definition,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Cloud continuum: The definition,

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:25:55.157956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae567002-dee2-419e-8019-c6c112efbfd5 · outbound

This paper cites Defog: fog computing benchmarks,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Defog: fog computing benchmarks,

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T11:25:55.283713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c61345b2-a6ce-4ffb-b1eb-9f3104168707 · outbound

This paper cites Kfiml: Kubernetes-based fog computing iot platform for online machine learning,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Kfiml: Kubernetes-based fog computing iot platform for online machine learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.166940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84c7563c-e099-48d1-a0ce-2b848aaf7001 · outbound

This paper cites Towards network-aware resource provisioning in kubernetes for fog computing applications,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Towards network-aware resource provisioning in kubernetes for fog computing applications,

Reference 18

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:56.135051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9c981ff3-7fd9-484a-bf21-34683e4215e8 · outbound

This paper cites Latency-aware industrial fog application orchestration with kubernetes,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Latency-aware industrial fog application orchestration with kubernetes,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.176762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9327f0fe-fddf-445e-a724-613fb980e768 · outbound

This paper cites Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption,

Reference 20

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:55.972196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation abacc880-e300-45b3-b374-e4cd92ad1f15 · outbound

This paper cites A survey of online failure prediction methods,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML A survey of online failure prediction methods,

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:55.868636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4a21e45e-f93a-4804-9c64-4442dc259f49 · outbound

This paper cites Chaos Engineering: A Multi-Vocal Literature Review.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Chaos Engineering: A Multi-Vocal Literature Review

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.190684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476e8500-39df-4215-be64-88225225714d · outbound

This paper cites Service level agreement in cloud computing: Taxonomy, prospects, and challenges,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Service level agreement in cloud computing: Taxonomy, prospects, and challenges,

Reference 23

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:55.761767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 45022ced-4f63-4f8e-a9f8-1c2ff52ec16a · outbound

This paper cites Benchmarking as empirical standard in software engineering research,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Benchmarking as empirical standard in software engineering research,

Reference 24

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:55.677866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc18bd65-e909-4e1e-92e6-8ee51cb4de2d · outbound

This paper cites Autoscaler evaluation and configuration: A practitioner’s guideline,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Autoscaler evaluation and configuration: A practitioner’s guideline,

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:25:55.605548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7781a4d-095d-477a-b183-9471fdc3ecf0 · outbound

This paper cites Process-Based Efficient Power Level Exporter,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Process-Based Efficient Power Level Exporter,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:25:56.932157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3ffc250f-79d9-4919-8ab7-813e66bbe993 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Imagenet: A large-scale hierarchical image database,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.215195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42ca8aa4-a1a9-43ca-8f1b-db66a4a55f10 · outbound

This paper cites Benchmarking distributed stream data processing systems,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Benchmarking distributed stream data processing systems,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T11:25:55.220322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e41cbcc2-c704-4bb8-8354-9c2f41b1b358 · outbound

This paper cites Ecoscape slis,.

Ecoscape: Fault Tolerance Benchmark for Adaptive Remediation Strategies in Real-Time Edge ML Ecoscape slis,

Reference 29

Resolution: verified exact; doi, observed 2026-08-06T11:25:55.266015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.