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DTSTART:19700308T020000
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DTSTAMP:20181221T160726Z
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:Workshop\nReproducibility, Scientific Computing, Scientific Wo
 rkflows, Workflows, Workshop Reg Pass, HPC, Data Intensive\n\nEnergy-Aware
  Workflow Scheduling and Optimization in Clouds Using Bat Algorithm\n\nGu,
  Budati\n\nWith the ever-increasing deployment of data centers and compute
 r networks around the world, cloud computing has emerged as one of the mos
 t important paradigms for large-scale data-intensive applications. However
 , these cloud environments face many challenges including energy consumpti
 on, execution time, heat and CO2 emission, and operation cost. Due to the 
 extremely large scale of these applications and a huge amount of resource 
 consumption, even a small portion of the improvements in any of the above 
 fields can yield huge ecological and financial rewards. Efficient and effe
 ctive workflow scheduling in cloud environments is one of the most signifi
 cant ways to confront the above problems and achieve optimal resource util
 ization. We propose an Energy Aware, Time and Throughput Optimization heur
 istic (EATTO) based on bat algorithm. Our goal is to minimize energy consu
 mption and execution time of computation-intensive workflows while maximiz
 ing throughput, without imposing any significant loss on the Quality of Se
 rvice (QoS) guarantee.
URL:https://sc18.supercomputing.org/presentation/?id=ws_works102&sess=sess
 163
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