Machine Learning for Cloud Management (Record no. 1432999)

MARC details
000 -LEADER
fixed length control field 01928nam a2200241 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250626122824.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250626b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781032939667
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Transcribing agency CSL
041 ## - LANGUAGE CODE
Source of code eng
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number D65,8(B):(S:72) R2
Assigning agency CSL
245 ## - TITLE STATEMENT
Title Machine Learning for Cloud Management
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Boca Raton :
Name of publisher, distributor, etc. CRC Press,
Date of publication, distribution, etc. 2022.
300 ## - PHYSICAL DESCRIPTION
Extent xxvi, 172p.
Other physical details : ill.
Dimensions ; 24 cm.
500 ## - GENERAL NOTE
General note Includes bibliography and index
520 ## - SUMMARY, ETC.
Summary, etc. Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Cloud computing.
9 (RLIN) 814702
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 480917
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Time series models.
9 (RLIN) 472681
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Neural networks.
9 (RLIN) 715389
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Kumar, Jitendra
Relator term author.
9 (RLIN) 700322
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Colon Classification (CC)
Suppress in OPAC No
Koha item type Textual
Classification part D65,8(B):(S:72) R2
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Colon Classification (CC)     Central Science Library Central Science Library 2024-11-06 Shivam book service   D65,8(B):(S:72) R2 SL1656052 2025-06-26 2025-06-26 Textual
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