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DTSTAMP:20260422T000711Z
LOCATION:405-406-407
DTSTART;TZID=America/Denver:20231114T113000
DTEND;TZID=America/Denver:20231114T120000
UID:submissions.supercomputing.org_SC23_sess169_pap467@linklings.com
SUMMARY:GreenNFV: Energy-Efficient Network Function Virtualization with Se
 rvice Level Agreement Constraints
DESCRIPTION:Md S. Q. Zulkar Nine and Tevfik Kosar (University at Buffalo),
  Muhammed Fatih Bulut (IBM TJ Watson Research Center), and Jinho Hwang (Me
 ta)\n\nNetwork Function Virtualization (NFV) platforms consume significant
  energy, introducing high operational costs in edge and data centers. This
  paper presents a novel framework called GreenNFV that optimizes resource 
 usage for network function chains using deep reinforcement learning. Green
 NFV optimizes resource parameters such as CPU sharing ratio, CPU frequency
  scaling, last-level cache (LLC) allocation, DMA buffer size, and packet b
 atch size. GreenNFV learns the resource scheduling model from the benchmar
 k experiments and takes Service Level Agreements (SLAs) into account to op
 timize resource usage models based on the different throughput and energy 
 consumption requirements. Our evaluation shows that GreenNFV models achiev
 e high transfer throughput and low energy consumption while satisfying var
 ious SLA constraints. Specifically, GreenNFV with Throughput SLA can achie
 ve 4.4X higher throughput and 1.5X better energy efficiency over the basel
 ine settings, whereas GreenNFV with Energy SLA can achieve 3X higher throu
 ghput while reducing energy consumption by 50%\n\nTag: Cloud Computing, Di
 stributed Computing, Energy Efficiency, Green Computing, Programming Frame
 works and System Software, State of the Practice, Sustainability\n\nRegist
 ration Category: Tech Program Reg Pass\n\nSession Chair: Marco Aldinucci (
 University of Torino, Italy; CINI HPC-KTT Laboratory)\n\n
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