Author: William David Louth

From Abstraction to Simplicity

Abstraction and simplification are two fundamental principles that often work together in the design of systems. With abstraction, we reduce system complexity by focusing on the essential aspects in the area of structure, elements, and behavior.

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The OSSification of Observability

Here we explore why the industry needs to move beyond the legacy tools and embrace a more dynamic and adaptable approach to gleaning genuine value from the ever-growing ocean of data collected.

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Climbing the Conceptual System

As engineering systems grow ever more complex, the engineering community's focus on simplistic measurement and reporting hinders achieving operational scalability by way of sensemaking and steering of such systems of systems.

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The Past, Present, and Future will be Simulated

The mirroring of software execution behavior, as performed by Simz (online) and Stenos (offline), has the potential to be one of the most significant advances in software systems engineering. Its impact could be as significant as that of distributed computing.

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Introducing Signals – The Next Big Thing

This post introduces the reasoning, thinking, and concepts behind a technology we call Signals, which we believe has the potential to have a profound impact on the design and development of software, the performance engineering of systems, and the management of distributed interconnected applications and services.

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Transcending Code, Containers, and Cloud

There is always tension between adaptability and structural stability in engineering and possibly life. We want our designs to be highly adaptable. With adaptation, our designs attempt to respond to change, sensed within the environment, intelligently with more change, though far more confined and possibly transient, at least initially. But there are limits to how far we can accelerate adaptation without putting incredible stress on the environment and the very system contained within.

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Beyond Big Data – Mirrored Algorithmic Simulation

Today, the stimulus used to develop machine intelligence is sensory data, which is transferred between devices and the cloud – the same data that concerns many consumers. But what if instead of sending data related to such things as a thermostat’s temperature set point, what was transmitted mostly concerned the action taken by the embedded software machine – an episodic memory of the algorithm itself?

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Circuits, Conduits, and Counters

Our brain houses billions of neurons (nerve cells) that communicate with each other through intricate networks of neural circuits. These circuits play a fundamental role in various cognitive functions, sensory processing, motor control, and generating thoughts and emotions. Why should it be different for Observability?

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Observability – The Significant Parts

Most current observability technologies don’t fair well as a source of behavioral signals or inferred states. They are not designed to reconstruct behavior that would allow the level of inspection we would need to translate from measurement to signal and, in turn, the state effectively. They are designed with data collection and reporting in mind of the event, not the signal or state.

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