{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/224893"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/224893","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"Armada: A Robust Latency-Sensitive Edge Cloud in Heterogeneous Edge-Dense Environments","abstract":"Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. We evaluate Armada in both real-world volunteer environments and emulated platforms to show how common edge applications, namely real-time object detection and face recognition, can be easily deployed on Armada serving distributed users at scale with low latency.","abstract_html":"Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. 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Major: Computer Science. Advisor: Abhishek Chandra. 1 computer file (PDF); vii, 46 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. We evaluate Armada in both real-world volunteer environments and emulated platforms to show how common edge applications, namely real-time object detection and face recognition, can be easily deployed on Armada serving distributed users at scale with low latency."]},{"key":"dc:title","label":"Title","values":["Armada: A Robust Latency-Sensitive Edge Cloud in Heterogeneous Edge-Dense Environments"]}]}],"canonical_facts":{"dc:creator":["Huang, Lei"],"dc:date.accessioned":["2021-10-13T17:54:09Z"],"dc:date.available":["2021-10-13T17:54:09Z"],"dc:date.issued":["2021-07"],"dc:description":["University of Minnesota M.S. thesis. 2021. Major: Computer Science. Advisor: Abhishek Chandra. 1 computer file (PDF); vii, 46 pages."],"dc:description.abstract":["Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading latency-sensitive and computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale in wide-area environments poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce Armada: a densely-distributed edge cloud infrastructure that explores the use of dedicated and volunteer resources to serve geo-distributed users in heterogeneous environments. We describe the lightweight Armada architecture and optimization techniques including performance-aware edge selection, auto-scaling and load balancing on the edge, fault tolerance, and in-situ data access. We evaluate Armada in both real-world volunteer environments and emulated platforms to show how common edge applications, namely real-time object detection and face recognition, can be easily deployed on Armada serving distributed users at scale with low latency."],"dc:identifier.uri":["https://hdl.handle.net/11299/224893"],"dc:language.iso":["en"],"dc:subject":["Edge Computing"],"dc:title":["Armada: A Robust Latency-Sensitive Edge Cloud in Heterogeneous Edge-Dense Environments"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:20:05Z"}