{"id":{"repo_id":"washington","oai_identifier":"oai:digital.lib.washington.edu:1773/42264"},"canonical_url":"https://search.dev.ndltd.org/etd/washington/oai:digital.lib.washington.edu:1773/42264","repository":{"repo_id":"washington","name":"University of Washington","base_url":"https://digital.lib.washington.edu/server/oai/request"},"display":{"title":"Failure Diagnosis for Datacenter Applications","abstract":"Fast and accurate failure diagnosis remains a major challenge for datacenter operators. Current datacenter applications are increasingly architected around loosely-coupled modular components: each component can scale and evolve independently. However, when application failures occur, they become much harder to detect and localize. The challenges are three-fold: complex component dependency, gray failures, and unpredictable component behaviors. My thesis is that fast and accurate failure diagnosis for datacenter applications is possible using three key ideas: (1) a global view of component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm that localizes both fail-stop and gray failures, and (3) a network architecture that produces predictable routes, simplifying failure localization without sacrificing load balancing and other network features. I present two complementary systems to demonstrate this. The first, Deepview, is a system that can localize virtual hard disk (VHD) failures in Infrastructure-as-a-Service clouds. I show that Deepview localizes VHD failures accurately and quickly to compute, storage and network components in production at Microsoft Azure. The second, Volur, is a network architecture that makes in-network routing predictable to the end-hosts. I show that Volur accurately localizes non-fail-stop link or switch failures and approximates state-of-the-art dynamic load balancing schemes.","abstract_html":"Fast and accurate failure diagnosis remains a major challenge for datacenter operators. Current datacenter applications are increasingly architected around loosely-coupled modular components: each component can scale and evolve independently. However, when application failures occur, they become much harder to detect and localize. The challenges are three-fold: complex component dependency, gray failures, and unpredictable component behaviors. My thesis is that fast and accurate failure diagnosis for datacenter applications is possible using three key ideas: (1) a global view of component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm that localizes both fail-stop and gray failures, and (3) a network architecture that produces predictable routes, simplifying failure localization without sacrificing load balancing and other network features. I present two complementary systems to demonstrate this. The first, Deepview, is a system that can localize virtual hard disk (VHD) failures in Infrastructure-as-a-Service clouds. I show that Deepview localizes VHD failures accurately and quickly to compute, storage and network components in production at Microsoft Azure. The second, Volur, is a network architecture that makes in-network routing predictable to the end-hosts. 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Current datacenter applications are increasingly architected around loosely-coupled modular components: each component can scale and evolve independently. However, when application failures occur, they become much harder to detect and localize. The challenges are three-fold: complex component dependency, gray failures, and unpredictable component behaviors. My thesis is that fast and accurate failure diagnosis for datacenter applications is possible using three key ideas: (1) a global view of component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm that localizes both fail-stop and gray failures, and (3) a network architecture that produces predictable routes, simplifying failure localization without sacrificing load balancing and other network features. I present two complementary systems to demonstrate this. The first, Deepview, is a system that can localize virtual hard disk (VHD) failures in Infrastructure-as-a-Service clouds. I show that Deepview localizes VHD failures accurately and quickly to compute, storage and network components in production at Microsoft Azure. The second, Volur, is a network architecture that makes in-network routing predictable to the end-hosts. I show that Volur accurately localizes non-fail-stop link or switch failures and approximates state-of-the-art dynamic load balancing schemes."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Failure Diagnosis for Datacenter Applications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Anderson, Thomas E.","Krishnamurthy, Arvind"],"dc:creator":["Zhang, Qiao"],"dc:date.accessioned":["2018-07-31T21:11:03Z"],"dc:date.available":["2018-07-31T21:11:03Z"],"dc:date.issued":["2018-07-31"],"dc:description":["Thesis (Ph.D.)--University of Washington, 2018"],"dc:description.abstract":["Fast and accurate failure diagnosis remains a major challenge for datacenter operators. Current datacenter applications are increasingly architected around loosely-coupled modular components: each component can scale and evolve independently. However, when application failures occur, they become much harder to detect and localize. The challenges are three-fold: complex component dependency, gray failures, and unpredictable component behaviors. My thesis is that fast and accurate failure diagnosis for datacenter applications is possible using three key ideas: (1) a global view of component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm that localizes both fail-stop and gray failures, and (3) a network architecture that produces predictable routes, simplifying failure localization without sacrificing load balancing and other network features. I present two complementary systems to demonstrate this. The first, Deepview, is a system that can localize virtual hard disk (VHD) failures in Infrastructure-as-a-Service clouds. I show that Deepview localizes VHD failures accurately and quickly to compute, storage and network components in production at Microsoft Azure. The second, Volur, is a network architecture that makes in-network routing predictable to the end-hosts. I show that Volur accurately localizes non-fail-stop link or switch failures and approximates state-of-the-art dynamic load balancing schemes."],"dc:format.mimetype":["application/pdf"],"dc:identifier.other":["Zhang_washington_0250E_18482.pdf"],"dc:identifier.uri":["http://hdl.handle.net/1773/42264"],"dc:language.iso":["en_US"],"dc:rights":["CC BY"],"dc:subject":["cloud computing","datacenter applications","datacenter networks","distributed systems","failure diagnosis","failure localization","Computer science"],"dc:title":["Failure Diagnosis for Datacenter Applications"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T05:58:27Z"}