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Extended Hoeffding Adaptive Tree based-Server Load Prediction in Cloud Computing environment

Published: 15 January 2020 Publication History

Abstract

Cloud Computing (CC) enables client-server relationship in order to release users from computational and storage responsibility. As multi-tenant environment, Cloud providers are dealing, in one hand, with multiple concurrent users each of which exhibits a different and variable behavior over time and in the other hand, with a performance interference due to the co-location of multiple virtual machines (VMs) in the same server. Therefore, a real time server load prediction is needed in order to ensure efficient resource provisioning. While classical data mining based techniques suffer from important evaluation time and are enable to react to changes as it arrives, stream mining techniques can provide a real time prediction and changes detection. Thus, in this paper we used a well known stream mining technique, Hoeffding Adaptive Tree (HAT), in order to provide real time server load prediction. The aim of our proposed technique is to detect and react on the fly to different kind of changes that can affect the server load. Therefore, we augmented HAT by ensemble drift detectors in order to produce more accurate prediction. In order to evaluate our proposed technique HAT-ADS, we first compared it with a well known load prediction technique based on Bayesian approach. Then we compared our solution with another HAT based techniques. Overall, The experimentation showed that HAT-ADS proved important flexibility to various types of changes providing high accuracy with quick evaluation time and small memory footprint.

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Cited By

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  • (2024)Fuzzy Min-Max Classifier in Cybersecurity ApplicationsAutomatic Documentation and Mathematical Linguistics10.3103/S000510552470025058:5(299-309)Online publication date: 1-Oct-2024
  • (2023)Designing Concept Drift Detection Ensembles: A Survey2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302492(1-10)Online publication date: 9-Oct-2023
  • (2021)An Online Model Integration Framework for Server Resource Workload Prediction2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS54544.2021.00053(414-421)Online publication date: Dec-2021

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cover image ACM Other conferences
HPCAsia '20: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region
January 2020
247 pages
ISBN:9781450372367
DOI:10.1145/3368474
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 15 January 2020

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Author Tags

  1. Cloud Computing
  2. Concept Drift
  3. Real-Time Prediction
  4. Stream Mining
  5. VM interference

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Overall Acceptance Rate 69 of 143 submissions, 48%

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Cited By

View all
  • (2024)Fuzzy Min-Max Classifier in Cybersecurity ApplicationsAutomatic Documentation and Mathematical Linguistics10.3103/S000510552470025058:5(299-309)Online publication date: 1-Oct-2024
  • (2023)Designing Concept Drift Detection Ensembles: A Survey2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302492(1-10)Online publication date: 9-Oct-2023
  • (2021)An Online Model Integration Framework for Server Resource Workload Prediction2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS54544.2021.00053(414-421)Online publication date: Dec-2021

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