Paper Citation Record · LEDGER

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.00929.

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

pith.paper-citation-record.v1
2506.00929 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:58:53.287452Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

25 of 25 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4af3b6a-6348-4001-bf78-de20e7d5a480 · outbound

This paper cites Dynamic resource allocation for cloud computing using reinforcement learning,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Dynamic resource allocation for cloud computing using reinforcement learning,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fc82c6a1-67ff-4fb6-a3ad-88979828abe3 · outbound

This paper cites Resource man- agement with deep reinforcement learning,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Resource man- agement with deep reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:58.624253Z

Source-reported events for the cited work

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Observation 315e80dc-2376-4f7a-9b23-46fba3256f65 · outbound

This paper cites A2c-drl: Dynamic scheduling for stochastic edge-cloud environments using a2c and deep reinforcement learning,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning A2c-drl: Dynamic scheduling for stochastic edge-cloud environments using a2c and deep reinforcement learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:58.380126Z

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Observation 90bded31-e85c-435e-9a22-99e61b633d5e · outbound

This paper cites Asynchronous methods for deep reinforcement learning,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Asynchronous methods for deep reinforcement learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:58.148939Z

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Observation ff47ac4b-1c00-445c-8b3d-6f59becbc7bb · outbound

This paper cites Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 974f3388-d02d-4723-a7cb-6feb6a6676a3 · outbound

This paper cites Balancing energy- efficiency and service quality for cloud applications,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Balancing energy- efficiency and service quality for cloud applications,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:57.675796Z

Source-reported events for the cited work

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Observation bb1b6438-c912-40ee-a6ec-fc1bcb89102c · outbound

This paper cites Dynamic vm placement and migration using machine learning in cloud,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Dynamic vm placement and migration using machine learning in cloud,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:57.427548Z

Source-reported events for the cited work

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Observation 5aef0479-87ee-48c8-a9dd-627e8f1fa2ac · outbound

This paper cites Enhancing Cloud Task Scheduling Using a Hybrid Particle Swarm and Grey Wolf Optimization Approach.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Enhancing Cloud Task Scheduling Using a Hybrid Particle Swarm and Grey Wolf Optimization Approach

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7cc1754f-a06a-4bd7-af4f-bae2c7bd4980 · outbound

This paper cites A game-theoretic method of fair resource allocation for cloud computing services,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning A game-theoretic method of fair resource allocation for cloud computing services,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:57.186772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d731d16-59cb-4bd5-8a97-b53a7ff377ec · outbound

This paper cites Resource allocation mechanisms and approaches on the internet of things,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Resource allocation mechanisms and approaches on the internet of things,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:56.927964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 84bfbd59-a39a-49f8-b337-0e810a0b88f4 · outbound

This paper cites Dynamic resource allocation method based on symbiotic organism search algorithm in cloud computing,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Dynamic resource allocation method based on symbiotic organism search algorithm in cloud computing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:56.691792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09f3a97f-61db-4621-a149-63333f8864cb · outbound

This paper cites Sg-pbfs: Shortest gap-priority based fair scheduling technique for job scheduling in cloud environment,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Sg-pbfs: Shortest gap-priority based fair scheduling technique for job scheduling in cloud environment,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:56.472386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 94afe0a9-6afc-4b63-8a1f-3e235994e218 · outbound

This paper cites Learning-based resource provisioning for cloud applications,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Learning-based resource provisioning for cloud applications,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:56.194838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8579a82c-276f-4cb5-a981-c921c1bc965d · outbound

This paper cites Integrated deep learning method for workload and resource prediction in cloud systems,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Integrated deep learning method for workload and resource prediction in cloud systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:55.926644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67e2a939-5061-4d6d-b715-8e4f81b0f047 · outbound

This paper cites Resource overbooking and application profiling in shared hosting platforms,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Resource overbooking and application profiling in shared hosting platforms,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:55.620208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 77871ce3-f305-4a4d-921a-195f2a3c7884 · outbound

This paper cites Optimal re- source allocation of cloud-based spark applications,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Optimal re- source allocation of cloud-based spark applications,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:55.424254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation faae9254-24b8-4e0f-b57c-f61f4ea138a0 · outbound

This paper cites Resource provi- sioning using workload clustering in cloud computing environment: a hybrid approach,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Resource provi- sioning using workload clustering in cloud computing environment: a hybrid approach,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:55.196690Z

Source-reported events for the cited work

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Observation 22fb0c66-cbd6-4d45-b50d-4e0d1c4f8635 · outbound

This paper cites An energy aware resource allocation based on combination of cnn and gru for virtual machine selection,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning An energy aware resource allocation based on combination of cnn and gru for virtual machine selection,

Reference 18

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Observation 78b9ba70-7c91-4017-bcf5-22f11432e6cf · outbound

This paper cites Machine and deep learning for resource allocation in multi-access edge computing: A survey,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Machine and deep learning for resource allocation in multi-access edge computing: A survey,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:54.967920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60535859-d3ec-4061-948d-08bf7ec80295 · outbound

This paper cites Aquatope: Qos-and-uncertainty- aware resource management for multi-stage serverless workflows,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Aquatope: Qos-and-uncertainty- aware resource management for multi-stage serverless workflows,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:54.677866Z

Source-reported events for the cited work

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Observation 0303c022-e3b0-4b57-bf77-43c00e079f81 · outbound

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Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Unresolved cited work

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Source-reported events for the cited work

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Observation 4faae277-62ef-4da0-a66c-b22f0b4b7a6e · outbound

This paper cites Resource allocation with workload-time windows for cloud-based software ser- vices: a deep reinforcement learning approach,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Resource allocation with workload-time windows for cloud-based software ser- vices: a deep reinforcement learning approach,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:54.488000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ff498765-fe0f-41c4-ae5c-fc4d3cfcb0b6 · outbound

This paper cites Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:54.282608Z

Source-reported events for the cited work

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Observation 271a78b6-ad6e-456e-819d-7928d8613815 · outbound

This paper cites A sufficient condition for convergences of adam and rmsprop,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning A sufficient condition for convergences of adam and rmsprop,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:53.962402Z

Source-reported events for the cited work

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Observation 7710c40d-354a-4ebb-b80f-e19080e8c755 · outbound

This paper cites Google cluster-usage traces: format+ schema,.

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning Google cluster-usage traces: format+ schema,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:58:53.727311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.