000			01928nam a2200241 4500
005			20250626122824.0
008			250626b |||||||| |||| 00| 0 eng d
020			_a9781032939667
040			_aCSL _cCSL
041			_2eng _aeng
084			_aD65,8(B):(S:72) R2 _qCSL
245			_aMachine Learning for Cloud Management
260			_aBoca Raton : _bCRC Press, _c2022.
300			_axxvi, 172p. _b: ill. _c; 24 cm.
500			_aIncludes bibliography and index
520			_aCloud 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			_aCloud computing. _9814702
650			_aMachine learning. _9480917
650			_aTime series models. _9472681
650			_aNeural networks. _9715389
700			_aKumar, Jitendra _eauthor. _9700322
942			_2CC _n0 _cTEXL _hD65,8(B):(S:72) R2
999			_c1432999 _d1432999