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Out-of-Distribution Representation Learning for Network System Forecasting.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Out-of-Distribution Representation Learning for Network System Forecasting./
Author:	Gao, Jianfei.
Description:	1 online resource (212 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
Subject:	Datasets. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30499164click for full text (PQDT)
ISBN:	9798379435196

Out-of-Distribution Representation Learning for Network System Forecasting.
Gao, Jianfei.

Out-of-Distribution Representation Learning for Network System Forecasting. - 1 online resource (212 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--Purdue University, 2023.

Includes bibliographical references

Representation learning algorithms, as the cutting edge of modern AIs, has shown their ability to automatically solve complex tasks in diverse fields including computer vision, speech recognition, autonomous driving, biology. Unsurprisingly, representation learning applications in computer networking domains, such as network management, video streaming, traffic forecasting, are enjoying increasing interests in recent years. However, the success of representation learning algorithms is based on consistency between training and test data distribution, which can not be guaranteed in some scenario due to resource limitation, privacy or other infrastructure reasons. Caused by distribution shift in training and test data, representation learning algorithms have to apply tuned models into environments whose data distribution are solidly different from the model training. This issue is addressed as Out-Of-Distribution (OOD) Generalization, and is still an open topic in machine learning. In this dissertation, I present solutions for OOD cases found in cloud services which will be beneficial to improve user experience. First, I implement Infinity SGD which can extrapolate from light-load server log to predict server performance under heavy-load. Infinity SGD builds the bridge between light-load and heavy-load server status through modeling server status under different loads by an unified Continuous Time Markov Chain (CTMC) of same parameters. I show that Infinity SGD can perform extrapolations that no precedent works can do on real-world testbed and synthetic experiments. Next, I propose Veritas, a framework to answer what will be the user experience if a different ABR, a kind of video streaming data transfer algorithm, was used with the same server, client and connection status. Veritas strictly follows Structural Causal Model (SCM) which guarantees its power to answer what-if counterfactual and interventional questions for video streaming. I showcase that Veritas can accurately answer confounders for what-if questions on real-world emulations where on existing works can. Finally, I propose time-then-graph, a provable more expressive temporal graph neural network (TGNN) than precedent works. We empirically show that time-then-graph is a more efficient and accurate framework on forecasting traffic on network data which will serve as an essential input data for Infinity SGD. Besides, paralleling with this dissertation, I formalize Knowledge Graph (KG) as doubly exchangeable attributed graph. I propose a doubly exchangeable representation blueprint based on the formalization which enables a complex logical reasoning task with no precedent works. This work may also find potential traffic classification applications in networking field.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379435196Subjects--Topical Terms:

3541416
Datasets.
Index Terms--Genre/Form:

542853
Electronic books.

Out-of-Distribution Representation Learning for Network System Forecasting.
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245 1 0 $a Out-of-Distribution Representation Learning for Network System Forecasting.
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300 $a 1 online resource (212 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
500 $a Advisor: Martins Ribeiro, Bruno Felisberto.
502 $a Thesis (Ph.D.)--Purdue University, 2023.
504 $a Includes bibliographical references
520 $a Representation learning algorithms, as the cutting edge of modern AIs, has shown their ability to automatically solve complex tasks in diverse fields including computer vision, speech recognition, autonomous driving, biology. Unsurprisingly, representation learning applications in computer networking domains, such as network management, video streaming, traffic forecasting, are enjoying increasing interests in recent years. However, the success of representation learning algorithms is based on consistency between training and test data distribution, which can not be guaranteed in some scenario due to resource limitation, privacy or other infrastructure reasons. Caused by distribution shift in training and test data, representation learning algorithms have to apply tuned models into environments whose data distribution are solidly different from the model training. This issue is addressed as Out-Of-Distribution (OOD) Generalization, and is still an open topic in machine learning. In this dissertation, I present solutions for OOD cases found in cloud services which will be beneficial to improve user experience. First, I implement Infinity SGD which can extrapolate from light-load server log to predict server performance under heavy-load. Infinity SGD builds the bridge between light-load and heavy-load server status through modeling server status under different loads by an unified Continuous Time Markov Chain (CTMC) of same parameters. I show that Infinity SGD can perform extrapolations that no precedent works can do on real-world testbed and synthetic experiments. Next, I propose Veritas, a framework to answer what will be the user experience if a different ABR, a kind of video streaming data transfer algorithm, was used with the same server, client and connection status. Veritas strictly follows Structural Causal Model (SCM) which guarantees its power to answer what-if counterfactual and interventional questions for video streaming. I showcase that Veritas can accurately answer confounders for what-if questions on real-world emulations where on existing works can. Finally, I propose time-then-graph, a provable more expressive temporal graph neural network (TGNN) than precedent works. We empirically show that time-then-graph is a more efficient and accurate framework on forecasting traffic on network data which will serve as an essential input data for Infinity SGD. Besides, paralleling with this dissertation, I formalize Knowledge Graph (KG) as doubly exchangeable attributed graph. I propose a doubly exchangeable representation blueprint based on the formalization which enables a complex logical reasoning task with no precedent works. This work may also find potential traffic classification applications in networking field.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Datasets. $3 3541416
650 4 $a User experience. $3 3543994
650 4 $a Bandwidths. $3 3560998
650 4 $a Graph representations. $3 3560730
650 4 $a Neural networks. $3 677449
650 4 $a Algorithms. $3 536374
650 4 $a Streaming media. $3 3564417
650 4 $a Servers. $3 3220915
650 4 $a Knowledge representation. $3 3689867
650 4 $a Markov analysis. $3 3562906
650 4 $a Computer science. $3 523869
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Purdue University. $3 1017663
773 0 $t Dissertations Abstracts International $g 84-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30499164 $z click for full text (PQDT)