{"689863":{"#nid":"689863","#data":{"type":"event","title":"PhD Defense by Anirudh Sarma","body":[{"value":"\u003Cdiv\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Performance-aware Resource Conservation Techniques for Geo-distributed Edge Infrastructures\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: Monday, April 20th, 2026\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 11:30 AM - 12:30 PM (Public portion) | 11:30 AM - 1:30 (Full)\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ELocation\u003C\/strong\u003E: Klaus Room 2100 (\u003Ca href=\u0022https:\/\/teams.microsoft.com\/meet\/27185961677505?p=Vrx9iormeyR8aGKJbi\u0022 id=\u0022OWA8c055e5b-ae6d-f40b-15ee-a3f6aa582982\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/teams.microsoft.com\/meet\/27185961677505?p=Vrx9iormeyR8aGKJbi\u0022\u003ETeams Meeting Link\u003C\/a\u003E)\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EAnirudh Sarma\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003ECS PhD Candidate\u003C\/div\u003E\u003Cdiv\u003EEmbedded Pervasive Laboratory | School of Computer Science\u003C\/div\u003E\u003Cdiv\u003ECollege of Computing | Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003EDr. Umakishore Ramachandran (Advisor) - School of Computer Science, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003EDr. Alexandros Daglis - School\u0026nbsp;of Computer Science, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003EDr. Alexey Tumanov - School\u0026nbsp;of Computer Science, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003EDr. Francisco Romero - School\u0026nbsp;of Computer Science, Georgia Institute of Technology\u003C\/div\u003E\u003Cdiv\u003EDr. Myungjin Lee - Cisco Research\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u003C\/div\u003E\u003Cdiv\u003EIn a future filled with immersive virtual\/augmented reality experiences and autonomous vehicles that need to respond to emergent situations, next-generation applications will generate enormous amounts of data and demand latency-sensitive processing to convert information to actionable knowledge. Edge computing has emerged to meet application demands where servers are deployed in geo-distributed sites close to the edge of the network, i.e., the source of information. Due to space and energy constraints, such edge sites have limited resources compared to traditional cloud datacenters.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ETo efficiently utilize the limited resources available at the edge, this dissertation proposes a granular resource management framework that conserves network, storage, and CPU resources at the Edge. The techniques enshrined in this framework are demonstrated via four main contributions -\u003C\/div\u003E\u003Cdiv\u003E1) \u003Cem\u003EClairvoyantEdge\u003C\/em\u003E, a networked-edge system that conserves network and storage resources; it prefetches static video data on to the edge servers, enables content reuse and delivery from edge servers to the clients via mmWave links thus reducing the burden on the backhaul bandwidth by 50% and consequently conserving the backhaul bandwidth for real-time traffic.\u003C\/div\u003E\u003Cdiv\u003E2) \u003Cem\u003EHarvestContainers\u003C\/em\u003E, a system that conserves CPU resources by harvesting spare CPU cores from latency-sensitive containers without impacting their performance and making such cores available for other throughput-oriented applications hosted at the edge site.\u003C\/div\u003E\u003Cdiv\u003E3) \u003Cem\u003EFEO\u003C\/em\u003E, a fully decentralized resource orchestrator that offloads computations (expressed as functions in the Function-as-a-Service paradigm) to neighboring edge sites with spare capacity to help meet their service level objectives (SLO) and conserve CPU resources across the geo-distributed edge infrastructure.\u003C\/div\u003E\u003Cdiv\u003E4) \u003Cem\u003EMISO\u003C\/em\u003E, in-memory state orchestrator that augments \u003Cem\u003EFEO\u003C\/em\u003E\u0026nbsp;to enable computation offload for stateful functions. Furthermore, it selectively migrates state to improve data locality and eliminate redundant network transfers, improving application performance with fewer compute and network resources.\u003C\/div\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EPerformance-aware Resource Conservation Techniques for Geo-distributed Edge Infrastructures\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Performance-aware Resource Conservation Techniques for Geo-distributed Edge Infrastructures"}],"uid":"27707","created_gmt":"2026-04-19 04:29:00","changed_gmt":"2026-04-19 04:29:47","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-20T11:30:51-04:00","event_time_end":"2026-04-20T12:03:51-04:00","event_time_end_last":"2026-04-20T12:03:51-04:00","gmt_time_start":"2026-04-20 15:30:51","gmt_time_end":"2026-04-20 16:03:51","gmt_time_end_last":"2026-04-20 16:03:51","rrule":null,"timezone":"America\/New_York"},"location":"Klaus Room 2100 (Teams Meeting Link)","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}