Scalable Error Isolation for Distributed Systems

In distributed systems, data corruption on a single node can propagate to other nodes in the system and cause severe outages. The probability of data corruption is already non-negligible today in large computer populations (e.g., in large datacenters). The resilience of processors is expected to decline in the near future, making it necessary to devise cost-effective software approaches to deal with data corruption.

In this paper, we present SEI, an algorithm that tolerates Arbitrary State Corruption (ASC) faults and prevents data corruption from propagating across a distributed system. SEI scales in three dimensions: memory, number of processing threads, and development effort. To evaluate development effort, fault coverage, and performance with our library, we hardened two realworld applications: a DNS resolver and memcached. Hardening these applications required minimal changes to the existing code base, and the performance overhead is negligible in the case of applications that are not CPUintensive, such as memcached. The memory overhead is negligible independent of the application when using ECC memory. Finally, SEI covers faults effectively: it detected all hardware-injected errors and reduced undetected errors from 44% down to only 0.15% of the software-injected computation errors in our experiments.