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DTSTART;TZID=America/Los_Angeles:20260814T083000
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DTSTAMP:20260811T162729Z
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SUMMARY:Krishnaswamy\, L. (CSE) - Network Load Balancing for Geographically Distributed Datacenters
DESCRIPTION:As datacenters scale up and become more geographically distributed\, wide-area network inter-datacenter traffic\, which typically consists of data-heavy tasks\, has become increasingly prevalent. Some of the noteworthy challenges raised by the coexistence and interaction between inter- and intra-datacenter traffic are the differences in their QoS requirements\, link utilization\, and round-trip times. To the best of our knowledge\, these challenges have not yet been addressed by current datacenter load balancers. To highlight this gap\, we conducted a comparative performance study of state-of-the-art datacenter load balancers. Through extensive simulations\, we study how they perform under different network topologies and workloads\, including intra-datacenter\, inter-datacenter\, and mixed intra- and inter-datacenter workloads that reflect how datacenters have evolved to keep up with their continuously changing driving application landscape. Our study shows that current load balancers are not able to adequately distribute load under inter-DC workloads as well as mixed intra- and inter-datacenter traffic coexistence.\nMotivated by our observations\, we introduce Balancia\, a transport agnostic\, lightweight network load balancer that dynamically switches between per-flow and per-packet control in order to provide adequate performance for both intra- and inter-DC traffic given their different characteristics and quality-of-service (QoS) requirements. We show that\, when compared against state-of-the-art load balancers\, Balancia achieves close to 80% reduction in the 99% tail flow completion times for inter-datacenter traffic in the presence of intra- and inter-datacenter workload coexistence. Further in this work\, we explore proactively monitoring for congestion with the help of phantom queues and rerouting flows in a timely manner. Through Balancia2.0 we decouple congestion control and load balancing signaling\, and examine its effects on DC and WAN traffic. \nEvent Host: Lakshmi Krishnaswamy\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Katia Obraczka \nZoom: https://ucsc.zoom.us/j/94414934371?pwd=7P3Umt0QQ930ESV02jMvCVHVbkIp9r.1 \nPasscode: 905786
URL:https://events.ucsc.edu/event/krishnaswamy-l-cse-network-load-balancing-for-geographically-distributed-datacenters/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260814T141500
DTEND;TZID=America/Los_Angeles:20260814T161500
DTSTAMP:20260810T163804Z
CREATED:20260810T163804Z
LAST-MODIFIED:20260810T163804Z
UID:10015329-1786716900-1786724100@events.ucsc.edu
SUMMARY:Aliamooei Lakeh\, S. (ECE) - Optimization and Decision-Support Frameworks for Resilient Power Systems Under Large-Scale Electrification
DESCRIPTION:The rapid electrification of transportation is creating new interdependencies between power and transportation systems\, particularly during extreme events and disasters. As electric vehicle (EV) adoption increases\, evacuation-related charging demand\, infrastructure disruptions\, and limited access to energy resources introduce challenges that conventional power system planning and operation frameworks were not designed to address. Wildfires provide a critical example: transmission outages and public safety power shutoffs can reduce network capacity while evacuation simultaneously concentrates charging demand along affected transportation corridors. Improving resilience therefore requires coordinated decision-making across the full disaster lifecycle\, from infrastructure preparedness to emergency operation and post-disaster recovery.\nThis research develops optimization and decision-support methods for resilient power systems under large-scale transportation electrification\, addressing three complementary stages of resilience. First\, the research will extend existing infrastructure planning models through a two-stage stochastic mixed-integer programming framework for the strategic siting and sizing of distributed generation\, energy storage systems\, and EV charging infrastructure under disaster uncertainty. Second\, building on a developed single-period nonlinear AC optimal power flow formulation\, the research will extend the framework to multi-period operation to coordinate priority-based EV evacuation charging with mobile EV charger dispatch during grid contingencies while explicitly representing voltage and thermal operating constraints. Third\, a mixed-integer routing and scheduling framework is proposed for the deployment of mobile energy resources\, including energy tankers and vehicle-to-everything (V2X)-capable fleets\, to support electric transportation and critical loads when conventional infrastructure is disrupted.\nTogether\, these components connect long-term infrastructure planning\, emergency grid operation\, and post-disaster energy recovery within an integrated optimization and decision-support framework. The research will build on preliminary results obtained using IEEE benchmark systems and will incorporate California case studies representing wildfire and flooding scenarios. Resilience will be evaluated using technical and operational metrics such as load not served\, priority-weighted EV energy served\, and recovery time. The overall goal is to provide decision-support tools for utilities\, transportation agencies\, and emergency planners to support resilient planning\, operation\, and recovery in increasingly electrified energy and transportation systems. \nEvent Host: Saeed Aliamooei Lakeh\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisors: Keith Corzine and Leila Parsa \nZoom: https://ucsc.zoom.us/j/95295718011?pwd=1Y5vBhoBX9V5OVQ3MJzy4FgyhtO9eb.1 \nPasscode: 545834
URL:https://events.ucsc.edu/event/aliamooei-lakeh-s-ece-optimization-and-decision-support-frameworks-for-resilient-power-systems-under-large-scale-electrification/
CATEGORIES:Ph.D. Presentations
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