Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning | Paper Citation Record

Observation 790a5c77-29d1-4299-81a9-4a7e89af6908 · outbound

This paper cites An overview on edge computing research,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning An overview on edge computing research,

Reference 1

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Observation cec8a9f5-ab0e-40e8-9e07-9dc7b469eb03 · outbound

This paper cites Towards automatic parameter tuning of stream processing systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Towards automatic parameter tuning of stream processing systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:56.560327Z

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Observation 5ee37a43-b11d-4702-b02f-6f8d47fb83ac · outbound

This paper cites Wasp: Wide-area adaptive stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Wasp: Wide-area adaptive stream processing,

Reference 3

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Observation aeee9185-2ac7-4a22-a1ae-c33787a7e83f · outbound

This paper cites Automatic performance tuning for distributed data stream processing systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Automatic performance tuning for distributed data stream processing systems,

Reference 4

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Observation 4565d526-48d1-4134-9941-d1af6822e32d · outbound

This paper cites Hierarchical auto- scaling policies for data stream processing on heterogeneous resources,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Hierarchical auto- scaling policies for data stream processing on heterogeneous resources,

Reference 5

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Observation 27b3e719-fa64-4877-8ff2-de5c15e80222 · outbound

This paper cites Towards evaluating stream processing autoscalers,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Towards evaluating stream processing autoscalers,

Reference 6

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This paper cites Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows,

Reference 7

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Observation e5c64db5-bfaa-4386-b7b8-21936b9115f1 · outbound

This paper cites Fas: A flow aware scaling mechanism for stream processing platform service based on lms,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Fas: A flow aware scaling mechanism for stream processing platform service based on lms,

Reference 8

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Observation 68f591cf-ccbf-4354-bedf-9c3a5a88ff12 · outbound

This paper cites Stream data load prediction for resource scaling using online support vector regression,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Stream data load prediction for resource scaling using online support vector regression,

Reference 9

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Observation 7dae47e1-34c8-4d35-ae6d-7d992cf1b9aa · outbound

This paper cites Recurrent concept drifts on data streams,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Recurrent concept drifts on data streams,

Reference 10

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Observation d0491c56-7de4-462a-9bed-f7f6d071fb11 · outbound

This paper cites Elastic data stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic data stream processing,

Reference 11

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Observation 4d0da283-5b39-4ac6-9232-d8dc94c45772 · outbound

This paper cites Model-based reinforcement learning for elastic stream processing in edge computing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Model-based reinforcement learning for elastic stream processing in edge computing,

Reference 12

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This paper cites Runtime adaptation of data stream processing systems: The state of the art,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Runtime adaptation of data stream processing systems: The state of the art,

Reference 13

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This paper cites Mead: Model-based vertical auto-scaling for data stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Mead: Model-based vertical auto-scaling for data stream processing,

Reference 14

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This paper cites Q-flink: A qos-aware controller for apache flink,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Q-flink: A qos-aware controller for apache flink,

Reference 15

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Observation ca60c5b0-0d85-42ed-9b84-14b7d74a450c · outbound

This paper cites Auto-sizing for stream processing applications at {LinkedIn},.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Auto-sizing for stream processing applications at {LinkedIn},

Reference 16

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This paper cites Turbine: Facebook’s service management platform for stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Turbine: Facebook’s service management platform for stream processing,

Reference 17

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Observation 683d0a86-857e-4763-9656-5775670c7cfa · outbound

This paper cites An optimal model for optimizing the placement and parallelism of data stream processing applications on cloud-edge computing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning An optimal model for optimizing the placement and parallelism of data stream processing applications on cloud-edge computing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:51.084969Z

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Observation b7b2d69d-7891-46e6-bf05-a511c4cabee6 · outbound

This paper cites Joint operator scaling and placement for distributed stream processing applications in edge computing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Joint operator scaling and placement for distributed stream processing applications in edge computing,

Reference 19

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This paper cites Elastic resource allocation based on dynamic perception of operator influence domain in distributed stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic resource allocation based on dynamic perception of operator influence domain in distributed stream processing,

Reference 20

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Observation 5f36c2c7-a0c2-4aaa-9511-b1d69743eb15 · outbound

This paper cites Optimal operator deployment and replication for elastic distributed data stream processing. concurr. comput.(2017).

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Optimal operator deployment and replication for elastic distributed data stream processing. concurr. comput.(2017)

Reference 21

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Observation 9c9d829c-3fa3-48e6-9919-65b1b428d04b · outbound

This paper cites Streamcloud: An elastic and scalable data streaming system,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Streamcloud: An elastic and scalable data streaming system,

Reference 22

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Observation c1e5c1b5-9334-4bfb-bd8d-fc8b362e0c01 · outbound

This paper cites Elastic scaling for data stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic scaling for data stream processing,

Reference 23

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This paper cites Elastic pulsar functions for distributed stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic pulsar functions for distributed stream processing,

Reference 24

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This paper cites Elastic symbiotic scaling of operators and resources in stream processing systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic symbiotic scaling of operators and resources in stream processing systems,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:49.690417Z

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Observation f24cb64a-5808-4722-9f1b-3e8f049e5c42 · outbound

This paper cites Proactive elasticity and energy aware- ness in data stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Proactive elasticity and energy aware- ness in data stream processing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:49.488147Z

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This paper cites Drs: Auto-scaling for real-time stream analytics,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Drs: Auto-scaling for real-time stream analytics,

Reference 27

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Observation ba30ff8a-f10a-478b-b68a-7f58d7f9eca6 · outbound

This paper cites Elastic stream processing with latency guarantees,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic stream processing with latency guarantees,

Reference 28

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This paper cites Feedback-control & queueing theory-based resource management for streaming applications,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Feedback-control & queueing theory-based resource management for streaming applications,

Reference 29

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This paper cites A stream-processing server with an internal and an external queue,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning A stream-processing server with an internal and an external queue,

Reference 30

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This paper cites Model-based scheduling for stream processing systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Model-based scheduling for stream processing systems,

Reference 31

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This paper cites Elastic complex event processing exploiting prediction,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Elastic complex event processing exploiting prediction,

Reference 32

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This paper cites Evaluation of load prediction techniques for distributed stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Evaluation of load prediction techniques for distributed stream processing,

Reference 33

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Observation 40ebf182-b292-4d32-adc0-f58331c3132a · outbound

This paper cites Caladrius: A performance modelling service for distributed stream processing systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Caladrius: A performance modelling service for distributed stream processing systems,

Reference 34

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This paper cites Qos-and contention- aware resource provisioning in a stream processing engine,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Qos-and contention- aware resource provisioning in a stream processing engine,

Reference 35

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Observation bc940a82-3a91-4dd5-adec-e4433bef1ef9 · outbound

This paper cites Cost-effective transfer learning for data streams,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Cost-effective transfer learning for data streams,

Reference 36

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This paper cites Multi-source transfer learning for non-stationary environments,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Multi-source transfer learning for non-stationary environments,

Reference 37

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This paper cites Proscale: Proactive autoscaling for microservice with time- varying workload at the edge,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Proscale: Proactive autoscaling for microservice with time- varying workload at the edge,

Reference 38

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Observation 1e4a3ba8-4180-42ee-b84b-b7ca15a44e3c · outbound

This paper cites Introduction to sequence learning models: Rnn, lstm, gru,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Introduction to sequence learning models: Rnn, lstm, gru,

Reference 39

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Observation 43daa98f-fbc2-413d-9bdc-4199d1ee1113 · outbound

This paper cites Deep learning for time series forecasting: a survey,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Deep learning for time series forecasting: a survey,

Reference 40

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This paper cites A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization,

Reference 41

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This paper cites Lstm and gru neural networks as models of dynamical processes used in predictive control: A comparison of models developed for two chemical reactors,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Lstm and gru neural networks as models of dynamical processes used in predictive control: A comparison of models developed for two chemical reactors,

Reference 42

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Observation ce3dcfdd-df08-4106-9463-8143acab6d8a · outbound

This paper cites A comparison between arima, lstm, and gru for time series forecasting,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning A comparison between arima, lstm, and gru for time series forecasting,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:45.208947Z

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This paper cites Implementing transfer learning across different datasets for time series forecasting,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Implementing transfer learning across different datasets for time series forecasting,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.910911Z

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Observation 00eb4f81-8638-4612-adb0-a2d4e5356d91 · outbound

This paper cites Predictive efficiency of arima and ann models: A case analysis of nifty fifty in indian stock market,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Predictive efficiency of arima and ann models: A case analysis of nifty fifty in indian stock market,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.681941Z

Source-reported events for the cited work

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Observation 5ecdbe1f-25c0-4e66-b94a-33588ebdff75 · outbound

This paper cites Rafferty, Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Rafferty, Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.530902Z

Source-reported events for the cited work

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Observation 4650e7c7-6c4d-43f9-bebb-6704ef16b995 · outbound

This paper cites A multi- source transfer learning model based on lstm and domain adaptation for building energy prediction,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning A multi- source transfer learning model based on lstm and domain adaptation for building energy prediction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.362094Z

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Observation dc61fe33-5829-4818-b5d9-825683bf2434 · outbound

This paper cites Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift

Reference 48

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Observation 6adb6a4e-2410-4a70-99fc-bc720f685a9c · outbound

This paper cites Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:58:42.984889Z

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 bbd70564-271a-455d-8fd7-4ce9b7abebd1 · outbound

This paper cites Investigating edge vs. cloud computing trade-offs for stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Investigating edge vs. cloud computing trade-offs for stream processing,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.209321Z

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 6979cd7f-ae23-4207-a564-5498bcc2882e · outbound

This paper cites A review on architecture and models for autonomic software systems,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning A review on architecture and models for autonomic software systems,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:44.002083Z

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 fed0f23d-d4ca-4421-bce6-b0b2f02b1b74 · outbound

This paper cites Some new observations on slo-aware edge stream processing,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Some new observations on slo-aware edge stream processing,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:43.838742Z

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 144742fc-0240-4759-bca0-9d76f586a672 · outbound

This paper cites Using stream processing to find suitable rides: An exploration based on new york city taxi data,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Using stream processing to find suitable rides: An exploration based on new york city taxi data,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:43.628888Z

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 0e648c43-9a34-47a2-934e-ce2c7b351c48 · outbound

This paper cites Efficient taxi and passenger searching in smart city using distributed coordination,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Efficient taxi and passenger searching in smart city using distributed coordination,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:43.449919Z

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 f96c50fa-992f-425a-bdca-85457772c3ac · outbound

This paper cites Model-based stream processing auto-scaling in geo-distributed environments,.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Model-based stream processing auto-scaling in geo-distributed environments,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:58:43.267909Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers