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UID:www.tcs.tifr.res.in/event/1334
DTSTAMP:20231031T050325Z
SUMMARY:Load Balancing under Data Locality: Extending Mean-Field Framework 
 to Constrained Large-Scale Systems
DESCRIPTION:Speaker: Debankur Mukherjee (Georgia Institute of Technology\, 
 Georgia)\n\nAbstract: \nLarge-scale parallel-processing infrastructures su
 ch as data centers and cloud networks form the cornerstone of the modern d
 igital environment. Central to their efficiency are resource management po
 licies\, especially load balancing algorithms (LBAs)\, which are crucial f
 or meeting stringent delay requirements of tasks. A contemporary challenge
  in designing LBAs for today's data centers is navigating data locality co
 nstraints that dictate which tasks are assigned to which servers. These co
 nstraints can be naturally modeled as a bipartite graph between servers an
 d various task types. Most LBA heuristics lean on the mean-field approxima
 tion's accuracy. However\, the non-exchangeability among servers induced b
 y the data locality invalidates this mean-field framework\, causing real-w
 orld system behaviors to significantly diverge from theoretical prediction
 s. From a foundational standpoint\, advancing our understanding in this do
 main demands the study of stochastic processes on large graphs\, thus need
 ing fundamental advancements in classical analytical tools.\nIn this prese
 ntation\, we will delve into recent advancements made in extending the acc
 uracy of mean-field approximation for a broad class of graphs. In particul
 ar\, we will talk about how to design resource-efficient\, asymptotically 
 optimal data locality constraints and how the system behavior changes fund
 amentally\, depending on whether the above bipartite graph is an expander\
 , a spatial graph\, or is inhomogeneous in nature.\n
URL:https://www.tcs.tifr.res.in/web/events/1334
DTSTART;TZID=Asia/Kolkata:20231121T160000
DTEND;TZID=Asia/Kolkata:20231121T170000
LOCATION:A-201 (STCS Seminar Room)
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