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DTSTART;TZID=America/Los_Angeles:20260423T173000
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DESCRIPTION:Datacenter workload fluctuations challenge the design and opera
 tion of our critical grid infrastructure. Significant gaps exist in assess
 ment of these fluctuations as to their lifetime and short-term impact on t
 he infrastructure and environment. Some mitigation approaches focus on thr
 ottling the datacenter workload\, thus impacting the performance. Other ap
 proaches include use of alternate generation sources or energy storage sys
 tems. While disparate\, these approaches are reactive and lack foresight a
 nd the intelligence to plan and strategize for optimal power management. W
 e introduce an approach to quantify the transitional entropy generated dur
 ing datacenter power fluctuations as a metric to evaluate datacenter perfo
 rmance using power demand measurements. A comparative assessment is provid
 ed between a BESS optimized datacenter and a regular datacenter to demonst
 rate the reduction of irreversibilities due to power fluctuations. Workloa
 ds are used to characterize the datacenter power demand at the point of in
 teraction with utility. This approach can be scaled from datacenters to se
 rvers to chips.\n\nSpeaker(s): Ratnesh K Sharma\, \n\nAgenda: \nNo-host so
 cial at 5:30pm\n\nPresentation at 6:00pm\n\nDinner at 7:00pm\n\nPresentati
 on continues at 7:45pm\n\nAdjourn by 8:30pm\n\nZio Fraedo's\, 611 Gregory 
 Lane\, Pleasant Hill\, California\, United States\, 94523
LOCATION:Zio Fraedo's\, 611 Gregory Lane\, Pleasant Hill\, California\, Uni
 ted States\, 94523
ORGANIZER:kent.liang@pge.com
SEQUENCE:26
SUMMARY:Characterizing 2nd Law Efficiency of AI Datacenters
URL;VALUE=URI:https://events.vtools.ieee.org/m/540124
X-ALT-DESC:Description: <br /><p><span style="font-family: 'Arial'\,sans-se
 rif\;">Datacenter workload fluctuations challenge the design and operation
  of our critical grid infrastructure. Significant gaps exist in assessment
  of these fluctuations as to their lifetime and short-term impact on the i
 nfrastructure and environment. Some mitigation approaches focus on throttl
 ing the datacenter workload\, thus impacting the performance. Other approa
 ches include use of alternate generation sources or energy storage systems
 . While disparate\, these approaches are reactive and lack foresight and t
 he intelligence to plan and strategize for optimal power management. We in
 troduce an approach to quantify the transitional entropy generated during 
 datacenter power fluctuations as a metric to evaluate datacenter performan
 ce using power demand measurements. A comparative assessment is provided b
 etween a BESS optimized datacenter and a regular datacenter to demonstrate
  the reduction of irreversibilities due to power fluctuations. Workloads a
 re used to characterize the datacenter power demand at the point of intera
 ction with utility. This approach can be scaled from datacenters to server
 s to chips.</span></p><br /><br />Agenda: <br /><p class="MsoNormal" style
 ="margin-left: 1.0in\; text-indent: -1.0in\;"><span style="font-family: 'A
 rial'\,sans-serif\;">No-host social at 5:30pm</span></p>\n<p class="MsoNor
 mal" style="margin-left: 1.0in\; text-indent: -1.0in\;"><span style="font-
 family: 'Arial'\,sans-serif\;">Presentation at 6:00pm</span></p>\n<p class
 ="MsoNormal" style="margin-left: 1.0in\; text-indent: -1.0in\;"><span styl
 e="font-family: 'Arial'\,sans-serif\;">Dinner at 7:00pm</span></p>\n<p cla
 ss="MsoNormal" style="margin-left: 1.0in\; text-indent: -1.0in\;"><span st
 yle="font-family: 'Arial'\,sans-serif\;">Presentation continues at 7:45pm<
 /span></p>\n<p class="MsoNormal" style="margin-left: 1.0in\; text-indent: 
 -1.0in\;"><span style="font-family: 'Arial'\,sans-serif\;">Adjourn by 8:30
 pm</span></p>
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