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Next-Generation Cloud Data Center Management : = Performance Optimization and Security Threat Mitigation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Next-Generation Cloud Data Center Management :/
Reminder of title:	Performance Optimization and Security Threat Mitigation.
Author:	Althoubi, Asaad Y.
Description:	1 online resource (98 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798382221151

Next-Generation Cloud Data Center Management : = Performance Optimization and Security Threat Mitigation.
Althoubi, Asaad Y.

Next-Generation Cloud Data Center Management :Performance Optimization and Security Threat Mitigation. - 1 online resource (98 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--Kent State University, 2024.

Includes bibliographical references

The rapid expansion of cloud computing has led to a surge in the requirement for effective resource management and strong security protocols in data centers. This dissertation presents three related research projects that together create a broad framework intended to improve many facets of cloud data center management.The framework initially suggests a comprehensive strategy for enhancing scheduling methods, which are essential for figuring out how well an application performs. This strategy entails creating a distributed scheduling plan that assigns jobs in a methodical manner according to predicted response times. A Truncated-Exponentially-Weighted-Moving-Average-based technique is also introduced by the framework. The mechanism is engineered to adjust to sudden influxes of tasks, leading to a significant decrease in response time distribution tails and an overall improvement in performance.Furthermore, the framework also tackles the problem of precisely forecasting response times in cloud data centers. It introduces SpearRidge, a machine learning model that makes use of sophisticated hybrid regularization technique along with Spearman Rank Correlation. SpearRidge's high accuracy and robustness are achieved through cross-validation tuning, which also improves response time prediction for more effective task assignment and optimized data center performance.The framework also integrates security measures to mitigate potential threats. It suggests a hybrid intrusion detection system (IDS) that combines artificial neural networks and the Gradient Boosting technique. By detecting subtle anomalies and adjusting to changing threats, the proposed strategy can improve cloud data center security by predicting and recognizing temporal trends in network traffic.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382221151Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Cloud data centerIndex Terms--Genre/Form:

554714
Electronic books.

Next-Generation Cloud Data Center Management : = Performance Optimization and Security Threat Mitigation.
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653 $a Security threats prediction
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