Paper Citation Record · LEDGER

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes

As of 22 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2507.05653.

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

pith.paper-citation-record.v1
2507.05653 v3

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:26:53.103341Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T07:47:34.585989Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T07:55:31.266019Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy23
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b514f4a-eca2-4b9a-b598-aa947bf85495 · outbound

This paper cites Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Serverless in the wild: Characterizing and optimizing the serverless workload at a large cloud provider,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:56.828293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af3daa20-28c3-4836-8c6d-4d58480136bd · outbound

This paper cites The state of serverless appli- cations: Collection, characterization, and community consensus,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes The state of serverless appli- cations: Collection, characterization, and community consensus,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:56.665032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3d1d6d36-7e37-4929-87d3-8414df9a66a2 · outbound

This paper cites Dynamic resource allocation in serverless architechtures using ai-based forecasting.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Dynamic resource allocation in serverless architechtures using ai-based forecasting

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:56.512553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 32ac2931-865f-4059-8456-afe01352842b · outbound

This paper cites Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:56.305363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 96907c5f-9262-4337-b835-ad98b72c75c6 · outbound

This paper cites Multi-level ml based burst-aware autoscaling for slo assurance and cost efficiency,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Multi-level ml based burst-aware autoscaling for slo assurance and cost efficiency,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T19:26:51.356280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebf898f2-b1aa-40b5-a426-42ee6e4cf8ba · outbound

This paper cites Harmonizing efficiency and practicability: optimizing resource utilization in serverless computing with jiagu,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Harmonizing efficiency and practicability: optimizing resource utilization in serverless computing with jiagu,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:56.057000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37223a14-85d9-4970-8de9-ef1935441dea · outbound

This paper cites With great freedom comes great opportunity: Rethinking resource allocation for serverless functions,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes With great freedom comes great opportunity: Rethinking resource allocation for serverless functions,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:55.855760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3d169cc3-d6da-4e42-9692-1d353ee3719c · outbound

This paper cites AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:26:51.596521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 767a9606-5091-4879-af38-c8c529d1cef6 · outbound

This paper cites KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:26:51.657931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa93f297-8dbd-414c-b67d-092599cb7359 · outbound

This paper cites Data pipeline approaches in serverless computing: a taxonomy, review, and research trends,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Data pipeline approaches in serverless computing: a taxonomy, review, and research trends,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:55.562767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f3bcdd7-8ef6-4c1e-9730-e6405df1f505 · outbound

This paper cites Serverless comput- ing: State-of-the-art and performance challenges,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Serverless comput- ing: State-of-the-art and performance challenges,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:55.361228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 64d9c3e8-386f-4668-bd89-7262c89794b6 · outbound

This paper cites (2023) Horizontal pod autoscaler.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes (2023) Horizontal pod autoscaler

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:55.201716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 70ec3a74-6f88-4170-a225-64799bb401a5 · outbound

This paper cites Thomperoo.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Thomperoo

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.906060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 018bee89-e1a1-4b49-876a-3e56719415df · outbound

This paper cites Cycle-stealing in load- imbalanced hpc applications,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Cycle-stealing in load- imbalanced hpc applications,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.780227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6b350e48-a32a-466b-a508-4f8846dacffc · outbound

This paper cites Syndeo: Portable ray clusters with secure containerization,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Syndeo: Portable ray clusters with secure containerization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.672392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 87a2dab3-23b7-4ca6-bf37-7f29ad06f127 · outbound

This paper cites Firm: An intelligent fine-grained resource management framework for slo-oriented microservices,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Firm: An intelligent fine-grained resource management framework for slo-oriented microservices,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.500894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d03c035b-f67b-489b-a23c-cb5828d49b99 · outbound

This paper cites Aware: Automate workload autoscaling with reinforcement learning in production cloud systems,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Aware: Automate workload autoscaling with reinforcement learning in production cloud systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.374034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee9b016a-6f73-4f60-b463-b567d936fb6e · outbound

This paper cites Magicscaler: Uncertainty-aware, predictive autoscaling,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Magicscaler: Uncertainty-aware, predictive autoscaling,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.246668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc3b1f17-9685-402d-8d1e-a0eb5dcbe341 · outbound

This paper cites Rl-based serverless container autoscaler,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Rl-based serverless container autoscaler,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.130658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a335486f-9b2a-4278-90c6-de4edca721a7 · outbound

This paper cites Snorkel: Rapid training data creation with weak supervision,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Snorkel: Rapid training data creation with weak supervision,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:54.020090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61c715a8-9f19-423d-a8a0-50571391a2e2 · outbound

This paper cites Enhancing machine learning- based autoscaling for cloud resource orchestration,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Enhancing machine learning- based autoscaling for cloud resource orchestration,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.910239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 11e4e08c-9687-4b84-a401-e01927986417 · outbound

This paper cites Collecting a large scale dataset for classifying fake news tweets using weak supervision,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Collecting a large scale dataset for classifying fake news tweets using weak supervision,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.788702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9838f841-ae38-4f86-8c8c-9c3980860589 · outbound

This paper cites Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.686296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6625db9-23eb-4272-b75c-8ee2b07f42c1 · outbound

This paper cites Machine learning for predictive resource scaling of micro-services,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Machine learning for predictive resource scaling of micro-services,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.558622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d63ca9d-8ca2-44e1-8792-1ce878d7415c · outbound

This paper cites Ai-powered cloud resource management: Machine learning for dynamic autoscaling and cost optimization,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Ai-powered cloud resource management: Machine learning for dynamic autoscaling and cost optimization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.426024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2831f621-646c-4056-ad0a-b46436bed643 · outbound

This paper cites Semi- supervised subspace learning for pattern classification via robust low rank constraint,.

AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes Semi- supervised subspace learning for pattern classification via robust low rank constraint,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:26:53.341278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 5b9c6b6a-84ab-4da5-b4d5-30f32272ff5f · inbound

BACC: Budget-Aware Calibration and Control for Horizontal Autoscaling cites this paper.

BACC: Budget-Aware Calibration and Control for Horizontal Autoscaling AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:55:31.268072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.