{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/76223"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/76223","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges","abstract":"In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives.","abstract_html":"In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives.","abstract_has_math":false,"creators":["Chemodanov, Dmitrii Yurievich"],"institution":"University of Missouri--Columbia","degree_name":"Ph. D.","degree_level":"Doctoral","degree_discipline":"Computer science (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Calyam, Prasah"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-24T03:07:01Z","subjects":[],"languages":["eng","English"],"rights":["OpenAccess."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/76223"],"render_values":[{"text":"https://doi.org/10.32469/10355/76223","href":"https://doi.org/10.32469/10355/76223","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/76223","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Calyam, Prasah"]},{"key":"dc:creator","label":"Author","values":["Chemodanov, Dmitrii Yurievich"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-08-25T19:02:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-08-25T19:02:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer science (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives."]},{"key":"dc:title","label":"Title","values":["Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges"]}]}],"canonical_facts":{"dc:contributor.advisor":["Calyam, Prasah"],"dc:creator":["Chemodanov, Dmitrii Yurievich"],"dc:date.accessioned":["2020-08-25T19:02:50Z"],"dc:date.available":["2020-08-25T19:02:50Z"],"dc:date.issued":["2019"],"dc:description":["Includes vita"],"dc:description.abstract":["In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives."],"dc:identifier.doi":["https://doi.org/10.32469/10355/76223"],"dc:identifier.uri":["https://hdl.handle.net/10355/76223"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["University of Missouri--Columbia"],"dc:rights":["OpenAccess."],"dc:title":["Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer science (MU)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph. 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