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:47@cds.iisc.ac.in DTSTART;TZID=Asia/Kolkata:20240404T160000 DTEND;TZID=Asia/Kolkata:20240404T170000 DTSTAMP:20240327T102422Z URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-04-ap ril-2024-intelligent-methods-for-cloud-workload-orchestration/ SUMMARY:M.Tech Research: Thesis Defense: CDS: 04\, April 2024 "Intelligent Methods for Cloud Workload Orchestration" DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nM.Tech Researc h Thesis Defense\n\n\n\nSpeaker : Mr. Prathamesh Saraf Vinayak\n\nS.R. Num ber : 06-18-01-10-22-21-1-19717\nTitle : "Intelligent Methods for Cloud Wo rkload Orchestration."\nResearch Supervisor :Dr. J. Lakshmi\nDate &\; T ime : April 04\, 2024 (Thursday)\, 04:00 PM\n\nVenue : The Thesis Défense will be held on MICROSOFT TEAMS\n\nPlease click on the following link to join the Thesis Defense:\n\nMS Teams link\n\n\n\nABSTRACT\n\nCloud workloa d orchestration is pivotal in optimizing the performance\, resource utiliz ation\, and cost-effectiveness of applications in data centers. As modern businesses and IT operations are migrating their businesses to the cloud\, understanding the dynamics of cloud data centers has become indispensable . Often\, two perspectives play a pivotal role in workload orchestration i n data centers. One is from the cloud provider side\, whose goal is to pro vision as many applications as possible on the available resources\, bidin g to SLA constraints and increasing return on investment. Others are from the side of enterprises and individual customers\, often referred to as en d users\, whose primary objective is to ensure application performance wit h a reduced deployment cost. Containerization has gained popularity for de ploying applications on public clouds\, where large enterprises manage num erous applications through thousands of containers placed onto Virtual Mac hines (VMs). While the need for cost-efficient placement in cloud data cen ters is undeniable\, the complexities involved in achieving this goal cann ot be understated. This problem is usually modeled as a multi-dimensional Vector Bin-packing Problem (VBP). Solving VBP optimally is NP-hard and pra ctical solutions requiring real-time decisions use heuristics. This work e xplores the landscape of cloud data centers\, emphasizing the significance of efficient bin packing in achieving optimal cost and resource utilizati on. Traditional methods\, including heuristics and optimal algorithms\, fa ce limitations in handling continuous request arrivals and the dynamic nat ure of cloud workloads. Integer Linear Programming (ILP)\, which can provi de optimal solutions for small problem sizes with tens of requests\, may t ake minutes to hours to complete\, even at such scales. Moreover\, optimal algorithms inherently demand perfect knowledge of all current and future requests to be placed within the bins\, rendering them unsuitable for the dynamic and often unpredictable online placement scenarios prevalent in cl oud setups.\n\nTo address these challenges\, this work introduces a novel approach to solving VBP through Reinforcement Learning (RL)\, trained on t he historical container workload trace for an enterprise\, a.k.a CARL (Cos t-optimized container placement using Adversarial Reinforcement Learning). The proposed work evaluates the effectiveness of CARL in comparison to tr aditional methods. CARL leverages historical container workload traces\, l earning from a semi-optimal VBP solver while optimizing VM costs. The cont ributions of this research extend beyond traditional methods\, providing i nsights into the advantages and disadvantages of heuristics\, optimal algo rithms\, and learning approaches. We trained and evaluated CARL on workloa ds derived from realistic traces from Google Cloud and Alibaba for placing 10\,000 container requests onto over 8000 VMs. CARL is fast\, making plac ement decisions for request sets with 124 containers per second within 65m s onto 1000s of potential VMs. It is also efficient\, achieving up to 13.9 8% lower VM costs than baseline heuristics for larger traces. To push the boundaries further\, we use the Mixture of Experts (MoE) strategy in CARL\ , wherein we use multiple experts who help CARL learn placement policies o f various approaches combined. Including an MoE strategy enhances CARL's a daptability to changes in workload distribution\, ensuring competitive per formance in scenarios with skewed resource needs or inter-arrival times.\n \n\n\nALL ARE WELCOME CATEGORIES:Events,Thesis Defense END:VEVENT BEGIN:VTIMEZONE TZID:Asia/Kolkata X-LIC-LOCATION:Asia/Kolkata BEGIN:STANDARD DTSTART:20230405T160000 TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE END:VCALENDAR