Paper Info
Title
Kraken: Leveraging Live Traffic Tests to Identify and Resolve Resource Utilization Bottlenecks in Large Scale Web Services.
Abstract
Modern web services such as Facebook are made up of hundreds of systems running in geographically-distributed data centers. Each system needs to be allocated capacity, configured, and tuned to use data center resources efficiently. Keeping a model of capacity allocation current is challenging given that user behavior and software components evolve constantly. Three insights motivate our work: (1) the live user traffic accessing a web service provides the most current target workload possible, (2) we can empirically test the system to identify its scalability limits, and (3) the user impact and operational overhead of empirical testing can be largely eliminated by building automation which adjusts live traffic based on feedback. We build on these insights in Kraken, a new system that runs load tests by continually shifting live user traffic to one or more data centers. Kraken enables empirical testing by monitoring user experience (e.g., latency) and system health (e.g., error rate) in a feedback loop between traffic shifts. We analyze the behavior of individual systems and groups of systems to identify resource utilization bottlenecks such as capacity, load balancing, software regressions, performance tuning, and so on, which can be iteratively fixed and verified in subsequent load tests. Kraken, which manages the traffic generated by 1.7 billion users, has been in production at Facebook for three years and has allowed us to improve our hardware utilization by over 20%.
Year
Venue
Field
2016
OSDI
User experience design,Load balancing (computing),Computer science,Kraken,Real-time computing,Component-based software engineering,Web service,Data center,Performance tuning,Distributed computing,Scalability
DocType
Citations 
PageRank 
Conference
12
0.61
References 
Authors
28
10
Name
Order
Citations
PageRank
Kaushik Veeraraghavan137220.81
Justin Meza241918.08
David Chou3161.69
Wonho Kim4120.61
Sonia Margulis5141.30
Scott Michelson6131.63
Rajesh Nishtala736524.36
Daniel Obenshain8130.95
Dmitri Perelman91207.40
Yee Jiun Song1036223.40