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ORGANIZER;CN="Satterfield, Mary Ann":mailto:msaterfi@purdue.edu
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ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Chaterji, 
 Somali":mailto:schaterji@purdue.edu
DESCRIPTION;LANGUAGE=en-US:Root Cause Analysis of Failures in Microservices
  through Causal Discovery\nAzam Ikram\nAdvised by Prof. Saurabh Bagchi\n\n
 Abstract: Most cloud applications use a large number of smaller sub-compon
 ents (called microservices) that interact with each other in the form of a
  complex graph to provide the overall functionality to the user. While the
  modularity of the microservice architecture is beneficial for rapid softw
 are development\, maintaining and debugging such a system quickly in cases
  of failure is challenging. We propose a scalable algorithm for rapidly de
 tecting the root cause of failures in complex microservice architectures. 
 The key ideas behind our novel hierarchical and localized learning approac
 h are: (1) to treat the failure as an intervention on the root cause to qu
 ickly detect it\, (2) only learn the portion of the causal graph related t
 o the root cause\, thus avoiding a large number of costly conditional inde
 pendence tests\, and (3) hierarchically explore the graph. The proposed te
 chnique is highly scalable and produces useful insights about the root cau
 se\, while the use of traditional techniques becomes infeasible due to hig
 h computation time. Our solution is application agnostic and relies only o
 n the data collected for diagnosis. For the evaluation\, we compare the pr
 oposed solution with a modified version of the PC algorithm and the state-
 of-the-art for root cause analysis. The results show a significant improve
 ment in top-k recall while significantly reducing the execution time.\n\nB
 io: I am a second-year Ph.D. student at ECE Purdue University working with
  Prof. Saurabh Bagchi. I'm broadly interested in large-scale distributed s
 ystems\, cloud computing\, computer networks\, and applied machine learnin
 g in systems. My current research work focuses on providing fault diagnosa
 bility for cloud applications through causal inference. Another part of my
  work concentrates on making the serverless framework more applicable to t
 he end user by reducing the latency and cost of serverless DAGs. Before jo
 ining Purdue\, I was at LUMS where I worked with Prof. Zafar Ayyub Qazi to
  improve the latency of cellular control plane messages of 4G/LTE.\n\n
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SUMMARY;LANGUAGE=en-US:CompE Seminar Series:  Azam Ikran:  Root Cause Analy
 sis of Failures in Microservices through Causal Discovery
DTSTART;TZID=Eastern Standard Time:20221108T120000
DTEND;TZID=Eastern Standard Time:20221108T133000
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20221107T160808Z
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STATUS:CONFIRMED
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