From Observability to Significance in Distributed Information Systems
It tackles the implicit issues in measurement principles for system monitoring, which is crucial for debugging and forensics in distributed information systems, though it appears incremental by summarizing existing work.
The paper addresses the challenge of modeling observability in distributed IT systems to understand process behavior, proposing a promise theoretic model based on distinguishability and causality to capture information transmission and loss across scales.
To understand and explain process behaviour we need to be able to see it, and decide its significance, i.e. be able to tell a story about its behaviours. This paper describes a few of the modelling challenges that underlie monitoring and observation of processes in IT, by human or by software. The topic of the observability of systems has been elevated recently in connection with computer monitoring and tracing of processes for debugging and forensics. It raises the issue of well-known principles of measurement, in bounded contexts, but these issues have been left implicit in the Computer Science literature. This paper aims to remedy this omission, by laying out a simple promise theoretic model, summarizing a long standing trail of work on the observation of distributed systems, based on elementary distinguishability of observations, and classical causality, with history. Three distinct views of a system are sought, across a number of scales, that described how information is transmitted (and lost) as it moves around the system, aggregated into journals and logs.