Rain or Shine? - Making Sense of Cloudy Reliability Data
Iyswarya Narayanan, Bikash Sharma, Di Wang, Sriram Govindan, Laura Caulfield, Anand Sivasubramaniam, Aman Kansal, Jie Liu, Badriddine Khessib and Kushagra Vaid
The Pennsylvania State University, Microsoft, Microsoft, Microsoft, Microsoft, The Pennsylvania State University, Microsoft, Microsoft, Microsoft, Microsoft

Cloud datacenters must ensure high availability for the hosted applications and failures can be the bane of datacenter operators. Understanding the what, when and why of failures can help tremendously to mitigate their occurrence and impact. Failures can, however, depend on numerous spatial and temporal factors spanning hardware, workloads, support facilities, and even the environment. One has to rely on failure data from the field to quantify the influence of these factors on failures. Towards this goal, we collect failures data along with many parameters that might influence failures from two large production datacenters with very diverse characteristics. We show that multiple factors simultaneously affect failures, and these factors may interact in non-trivial ways. This makes conventional approaches that study aggregate characteristics or single parameter influences, rather inaccurate. Instead, we build a multi-factor analysis framework to systematically identify influencing factors, quantify their relative impact, and help in more accurate decision making for failure mitigation. We demonstrate this approach for three important decisions: spare capacity provisioning, comparing the reliability of hardware for vendor selection, and quantifying flexibility in datacenter climate control for cost-reliability trade-offs.