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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20251215T032855Z
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DTSTART;TZID=America/New_York:20251204T173000
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DESCRIPTION:Abstract:\n\nIn today's ecosystem of deeply interconnected micr
 oservices\, the reliance on downstream dependencies presents a critical vu
 lnerability: the downtime of a single service can trigger a cascading fail
 ure\, severely impacting upstream operations and the end customer experien
 ce. This abstract proposes the Intelligent and Predictive Failover mechani
 sm as a robust solution to mitigate this systemic risk. By intelligently c
 lustering services and applying proactive monitoring with advanced failove
 r predictability\, this approach aims to identify potential errors and imp
 ending failures before they are manifested through an actual customer requ
 est. The focus will be on the technical implementation of intelligent anom
 aly tracking coupled with health checks and threshold-triggered alarms. Fu
 rthermore\, we will delve into sophisticated latency-based region detectio
 n techniques that enable rapid and intelligent traffic failover and failba
 ck capabilities. Ultimately\, this presentation will demonstrate how to le
 verage these patterns to predict failures\, significantly reduce customer 
 impact\, and continuously improve system resilience based on observed patt
 erns.\n\nCo-sponsored by: IEEE Computer Society\n\nSpeaker(s): Teja Swaroo
 p Mylavarapu\n\nAgenda: \n5:30 PM Start\n\n6:15 PM QA\n\n6:30 PM Session C
 omplete\n\nVirtual: https://events.vtools.ieee.org/m/516338
LOCATION:Virtual: https://events.vtools.ieee.org/m/516338
ORGANIZER:vishal.ras@gmail.com
SEQUENCE:21
SUMMARY:Intelligent and Predictive Failover - The race against Errors by ma
 rching towards Resiliency..!!
URL;VALUE=URI:https://events.vtools.ieee.org/m/516338
X-ALT-DESC:Description: <br /><p>Abstract:</p>\n<p>In today's ecosystem of 
 deeply&nbsp\;<strong>interconnected microservices</strong>\, the reliance 
 on downstream dependencies presents a critical vulnerability: the downtime
  of a single service can trigger a cascading failure\, severely impacting 
 upstream operations and the end customer experience. This abstract propose
 s the <strong>Intelligent and Predictive Failover</strong> mechanism as a 
 robust solution to mitigate this systemic risk. By intelligently <strong>c
 lustering services</strong> and applying <strong>proactive monitoring</str
 ong> with advanced <strong>failover predictability</strong>\, this approac
 h aims to identify potential errors and impending failures <em>before</em>
  they are manifested through an actual customer request. The focus will be
  on the technical implementation of <strong>intelligent anomaly tracking</
 strong> coupled with health checks and <strong>threshold-triggered alarms<
 /strong>. Furthermore\, we will delve into sophisticated <strong>latency-b
 ased region detection</strong> techniques that enable rapid and intelligen
 t traffic <strong>failover and failback</strong> capabilities. Ultimately\
 , this presentation will demonstrate how to leverage these patterns to <st
 rong>predict failures</strong>\, significantly <strong>reduce customer imp
 act</strong>\, and continuously <strong>improve system resilience</strong>
  based on observed patterns.</p><br /><br />Agenda: <br /><p>5:30 PM Start
 </p>\n<p>6:15 PM QA</p>\n<p>6:30 PM Session Complete</p>
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