Failure Diagnosis for Datacenter Applications

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.

Author dc:creator

• Zhang, Qiao

Advisors dc:contributor.advisor

• Anderson, Thomas E.
• Krishnamurthy, Arvind

• cloud computing
• datacenter applications
• datacenter networks
• distributed systems
• failure diagnosis
• failure localization
• Computer science

Statement dc:rights

• CC BY

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1773/42264

OAI identifier oai:identifier

oai:digital.lib.washington.edu:1773/42264