Stacked generalization based meta-classifier for prediction of cloud workload (Record no. 22719)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250425095231.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250425b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25993 |
| Author | Singh, Sanjay T. |
| 245 ## - TITLE STATEMENT | |
| Title | Stacked generalization based meta-classifier for prediction of cloud workload |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(4), Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3340-3346p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Cloud computing has revolutionized the way software, platforms, and<br/>infrastructure can be acquired by making them available as on-demand<br/>services that can be accessed from anywhere via a web browser. Due to<br/>its ubiquitous nature Cloud data centers continuously experience<br/>fluctuating workloads which demands for dynamic resource<br/>provisioning. These workloads are either placed on Virtual Machines<br/>(VMs) or containers which abstract the underlying physical resources<br/>deployed at the data center. A proactive or reactive method can be used<br/>to allot required resources to the workload. Reactive approaches tend<br/>to be inefficient as it takes a significant amount of time to configure the<br/>resources to meet the change in demands. A proactive approach for<br/>resource management is better in meeting workload demands as it<br/>makes an appropriate number of resources available in advance to<br/>cater to the fluctuations in workload. The success of such an approach<br/>relies on the ability of the resource management module of a data<br/>center to accurately predict future workloads. Machine Learning (ML)<br/>has already proven itself to be very effective in performing prediction<br/>in various domains. In this work, we propose an ML meta-classifier<br/>based on stacked generalization for predicting future workloads<br/>utilising the past workload trends which are recorded as event logs at<br/>Cloud data centers. The proposed model showed a prediction accuracy<br/>of 98.5% indicating its applicability for the Cloud environment where<br/>SLA requirements must be closely adhered to. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25994 |
| Co-Author | Tiwari, Mahendra |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_14_Iss_4_Paper_6_3340_3346.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 25/04/2025 | 2025-0668 | 25/04/2025 | 25/04/2025 | Articles Abstract Database |