The underlying observability model is the primary reason for distributed tracing, metrics, and event logging failing to deliver much-needed capabilities and benefits to systems engineering teams. There is no natural or inherent way to transform and scale such observability data collection analysis to generate signals and inferring states.
Author: William David Louth
Humanizing Observability and Controllability
Humanism is a philosophical stance at the heart of what Humainary aims to bring to service management operations. It runs counter to the misguided trend of wanton and wasteful extensive data collection so heavily touted by those focused on selling a service rather than solving a problem, now and in the future.
Simplicity and Significance in Observability
As computing and complexity scaled up, the models and methods should have reduced and simplified the communication and control surface area between man and machine. Instead, monitoring (passive) and management (reactive) solutions have lazily reflected the complexity's nature at a level devoid of simplicity and significance but instead polluted with noise.
Observability – A Multitude of Memories
There are at least two distinct paths to the future of observability. One path that would continue increasing the volume of collected data in its attempt to reconstruct reality in high-definition on a single plane with little consideration for effectiveness or efficiencies. Another would focus on seeing the big picture in near-real-time from the perspective of human or artificial agents.
AIOps – A Postmodern Observability Model
We propose a model which can better serve site engineering reliability and service operations by being foundational to developing situational awareness capabilities and system resilience capacities, particularly adaptability and experimentation, as in dynamic configuration and chaos engineering.
AIOps – The Double Cone Model
The Double Cone Model is a valuable conceptualization in thinking about more efficient and effective methods to handle data overload and generate far more actionable insight from a model much closer to how the human mind reasons about physical and social spaces.
AIOps – Visibility and Cognition
All points of experience within a topology offer some visibility, but the language (codes, syntax) and model (concepts) employed can differ greatly. This is problematic when the goal is to determine the intent and outcome of an interaction's operation(s).
AIOps – Why Service Cognition?
Today's data, such as logs, traces, and metrics, are too far removed to be the basis for a language and model that illuminates the dynamic nature of service interaction and system stability inference and state prediction formed across distributed agents.
AIOps – The Observer
Observability is purposefully seeing a system in terms of operations and outcomes. In control theory, this is sometimes simplified to monitoring inputs and outputs, with the comparative prediction of the output from input, possibly factoring in history.
The Intelligence in AIOps
It could be argued that no one fully understands what AIOps pertains to now in its aspirational rise within the IT management industry and community. AIOps is a moving target and a term hijacked by Observability vendor marketing. It's hard to pin down.