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DTSTAMP:20260522T150127Z
LOCATION:D173
DTSTART;TZID=America/Chicago:20181111T114500
DTEND;TZID=America/Chicago:20181111T115500
UID:submissions.supercomputing.org_SC18_sess163_ws_works102@linklings.com
SUMMARY:Energy-Aware Workflow Scheduling and Optimization in Clouds Using 
 Bat Algorithm
DESCRIPTION:Yi Gu and Chandu Budati (Middle Tennessee State University)\n\
 nWith the ever-increasing deployment of data centers and computer networks
  around the world, cloud computing has emerged as one of the most importan
 t paradigms for large-scale data-intensive applications. However, these cl
 oud environments face many challenges including energy consumption, execut
 ion time, heat and CO2 emission, and operation cost. Due to the extremely 
 large scale of these applications and a huge amount of resource consumptio
 n, even a small portion of the improvements in any of the above fields can
  yield huge ecological and financial rewards. Efficient and effective work
 flow scheduling in cloud environments is one of the most significant ways 
 to confront the above problems and achieve optimal resource utilization. W
 e propose an Energy Aware, Time and Throughput Optimization heuristic (EAT
 TO) based on bat algorithm. Our goal is to minimize energy consumption and
  execution time of computation-intensive workflows while maximizing throug
 hput, without imposing any significant loss on the Quality of Service (QoS
 ) guarantee.\n\nTag: Reproducibility, Scientific Computing, Scientific Wor
 kflows, Workflows, HPC, Data Intensive\n\nRegistration Category: Workshop 
 Reg Pass\n\n
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