BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
TZID:Asia/Kolkata
X-WR-TIMEZONE:Asia/Kolkata
BEGIN:VEVENT
UID:24@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20231221T110000
DTEND;TZID=Asia/Kolkata:20231221T120000
DTSTAMP:20231215T045612Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-in
 telligent-methods-for-cloud-workload-orchestration/
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : "Intelligent Methods for
  Cloud Workload Orchestration."
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research 
 Thesis Colloquium\n\n\n\nSpeaker : Mr. Prathamesh Saraf Vinayak\n\nS.R. Nu
 mber : 06-18-01-10-22-21-1-19717\n\nTitle :"Intelligent Methods for Cloud 
 Workload Orchestration"\nResearch Supervisor: Dr. Lakshmi J\nDate &amp\; T
 ime : December 21\, 2023 (Thursday) at 11:00 AM\nVenue : # 102 CDS Seminar
  Hall\n\n\n\nABSTRACT\n\nCloud workload orchestration is pivotal in optimi
 zing the performance\, resource utilization\, and cost-effectiveness of ap
 plications in data centers. As modern businesses and IT operations are mig
 rating their businesses to the cloud\, understanding the dynamics of cloud
  data centers has become indispensable. Often\, two perspectives play a pi
 votal role in workload orchestration in data centers. One is from the clou
 d provider side\, whose goal is to provision as many applications as possi
 ble on the available resources\, biding to SLA constraints and increasing 
 return on investment. Others are from the side of enterprises and individu
 al customers\, often referred to as end users\, whose primary objective is
  to ensure application performance with a reduced deployment cost. Contain
 erization has gained popularity for deploying applications on public cloud
 s\, where large enterprises manage numerous applications through thousands
  of containers placed onto Virtual Machines (VMs). While the need for cost
 -efficient placement in cloud data centers is undeniable\, the complexitie
 s involved in achieving this goal cannot be understated. This problem is u
 sually modeled as a multi-dimensional Vector Bin-packing Problem (VBP). So
 lving VBP optimally is NP-hard and practical solutions requiring real-time
  decisions use heuristics. This work explores the landscape of cloud data 
 centers\, emphasizing the significance of efficient bin packing in achievi
 ng optimal cost and resource utilization. Traditional methods\, including 
 heuristics and optimal algorithms\, face limitations in handling continuou
 s request arrivals and the dynamic nature of cloud workloads. Integer Line
 ar Programming (ILP)\, which can provide optimal solutions for small probl
 em sizes with tens of requests\, may take minutes to hours to complete\, e
 ven at such scales. Moreover\, optimal algorithms inherently demand perfec
 t knowledge of all current and future requests to be placed within the bin
 s\, rendering them unsuitable for the dynamic and often unpredictable onli
 ne placement scenarios prevalent in cloud setups. To address these challen
 ges\, this work introduces a novel approach to solving VBP through Reinfor
 cement Learning (RL)\, trained on the historical container workload trace 
 for an enterprise\, a.k.a CARL (Cost-optimized container placement using A
 dversarial Reinforcement Learning). The proposed work evaluates the effect
 iveness of CARL in comparison to traditional methods. CARL leverages histo
 rical container workload traces\, learning from a semi-optimal VBP solver 
 while optimizing VM costs. The contributions of this research extend beyon
 d traditional methods\, providing insights into the advantages and disadva
 ntages of heuristics\, optimal algorithms\, and learning approaches. We tr
 ained and evaluated CARL on workloads derived from realistic traces from G
 oogle Cloud and Alibaba for placing 10\,000 container requests onto over 8
 000 VMs. CARL is fast\, making placement decisions for request sets with 1
 24 containers per second within 65ms onto 1000s of potential VMs. It is al
 so efficient\, achieving up to 13.98% lower VM costs than baseline heurist
 ics for larger traces. To push the boundaries further\, we use the Mixture
  of Experts (MoE) strategy in CARL\, wherein we use multiple experts who h
 elp CARL learn placement policies of various approaches combined. Includin
 g an MoE strategy enhances CARL's adaptability to changes in workload dist
 ribution\, ensuring competitive performance in scenarios with skewed resou
 rce needs or inter-arrival times.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events
END:VEVENT
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
X-LIC-LOCATION:Asia/Kolkata
BEGIN:STANDARD
DTSTART:20221221T110000
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
END:STANDARD
END:VTIMEZONE
END:VCALENDAR