{"677891":{"#nid":"677891","#data":{"type":"event","title":"PhD Proposal by Jin Heo","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Adaptively Serving XR Workloads from Resource-constrained Edge\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate\u003C\/strong\u003E: Tuesday, Oct 29, 2024\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime\u003C\/strong\u003E: 11:30 AM \u2013 1:00 PM EST\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation (Hybrid)\u003C\/strong\u003E: KACB 3402 and MS Teams (\u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NGEwNDMyMzEtZjc0OC00NTBhLTk0OGItYTFlNDVjMjRjMWM4%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%223bec97d9-f9aa-4ec9-a105-96d8e1276e1c%22%7d\u0022 title=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NGEwNDMyMzEtZjc0OC00NTBhLTk0OGItYTFlNDVjMjRjMWM4%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%223bec97d9-f9aa-4ec9-a105-96d8e1276e1c%22%7d\u0022\u003Elink\u003C\/a\u003E) \u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EJin Heo\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPh.D. Student\u003C\/p\u003E\u003Cp\u003ESchool of Computer Science\u003C\/p\u003E\u003Cp\u003ECollege of Computing\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Ada Gavrilovska (advisor) - School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Alexey Tumanov - School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Ashutosh Dhekne - School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Ketan Bhardwaj - School of Computer Science, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EExtended reality (XR) applications demand high computational power for perception and 3D rendering, often exceeding the capabilities of modern mobile devices. Edge computing offers a solution by allowing the offloading of intensive tasks to nearby servers. However, the resource constraints of edge infrastructures present significant challenges in delivering high-quality, multi-user XR experiences. The diverse deployment environments of XR applications often make the current static workload distribution methods suboptimal. Additionally, compression methods for emerging sensor data that do not consider real-time XR constraints compromise the benefit of edge assistance.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis research proposes that to deliver high-quality, multi-user mobile XR experiences via edge computing, the XR serving system at the edge needs to be adaptive and coordinate the distribution and optimization of the XR workloads jointly with the data transfer, using quality metrics that capture the end-user experience. To address the challenges in edge computing for XR, I introduce a framework for flexibly distributing XR workloads, a fast and lightweight LiDAR compression method for offloading online perception tasks to the edge, and propose adaptive workload optimization techniques for multi-user serving. These include solutions for 3D rendering optimization and an adaptive system for perception scheduling.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe introduced framework facilitates efficient edge-assisted XR across diverse deployment environments by enabling runtime adaptation of workload distribution. The LiDAR compression method balances compression quality with latency, enhancing real-time LiDAR perception offloading to the edge. The proposed adaptive workload optimization techniques, which consider the user-side quality of service (QoS), improve the multi-user serving capabilities of resource-constrained edge servers while maintaining the QoS. This research demonstrates edge computing\u0027s potential to enable the widespread adoption of responsive, high-quality XR applications across diverse scenarios.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAdaptively Serving XR Workloads from Resource-constrained Edge\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Adaptively Serving XR Workloads from Resource-constrained Edge "}],"uid":"27707","created_gmt":"2024-10-24 14:59:31","changed_gmt":"2024-10-24 15:00:29","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-29T11:30:00-04:00","event_time_end":"2024-10-29T13:30:00-04:00","event_time_end_last":"2024-10-29T13:30:00-04:00","gmt_time_start":"2024-10-29 15:30:00","gmt_time_end":"2024-10-29 17:30:00","gmt_time_end_last":"2024-10-29 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"KACB 3402 and MS Teams ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"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":""}}}