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.
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.
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.
For Humainary, the goal is to encourage as much as possible the analytical processing of observations at the source of event emittance and in the moment of the situation. To propagate assessments, not data.
Since the very beginning of the hype of Observability, we have contended that the link with Controllability must be maintained for there ever to be a return on investment (ROI) that matches the extravagant claims from vendors pushing a message of more-is-better.
This two-part series will discuss critical factors that weighed heavily in our rethinking of Observability and how they manifest in our toolkit under the headings: conceptualization, communication, coordination, collaboration, and cognition.
The Humainary project aims to bring much-needed sensibility, streamlining, simplicity, and sophistication back to an area that seems to fight forcefully not to move past yesteryear technologies like logging and tracing.
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.
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.
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.
For Humainary, the goal is to encourage as much as possible the analytical processing of observations at the source of event emittance and in the moment of the situation. To propagate assessments, not data.
We have divided up our mission for the Humainary Toolkit into three phases, with each phase mapping to one or more layers: Measure, Model, and Memory
Since the very beginning of the hype of Observability, we have contended that the link with Controllability must be maintained for there ever to be a return on investment (ROI) that matches the extravagant claims from vendors pushing a message of more-is-better.
This two-part series will discuss critical factors that weighed heavily in our rethinking of Observability and how they manifest in our toolkit under the headings: conceptualization, communication, coordination, collaboration, and cognition.
The Humainary project aims to bring much-needed sensibility, streamlining, simplicity, and sophistication back to an area that seems to fight forcefully not to move past yesteryear technologies like logging and tracing.