Pronab Pal, Keybyte Systems, Australia
In today's cloud-native multi-core environments, the deployment and runtime performance of applications often take precedence over the visibility and agility of business logic. While this prioritization ensures optimal responsiveness, it can inadvertently create barriers to real-time analysis and rapid business adaptability. Traditional approaches necessitate reproducing issues in development environments and implementing additional tracing mechanisms, leading to delays in problem resolution and business evolution. This paper introduces the Prompt and Response (PnR) computing model. This paradigm shift addresses these challenges by maintaining a clear representation of intention flow and object data throughout the application lifecycle. The PnR system enables real-time analysis and intelligence derivation in production environments, transcending the limitations of container boundaries and module isolation. By representing every input and result of each module associated with distinct intentions within the PnR framework, we create a unified and traceable computational space called Intention Space. This approach allows for precise identification and analysis of specific modules referred to as just 'Design Chunks', regardless of their distribution across single or multiple containers or boundaries. We explore the architectural patterns of PnR transformations, illustrating how this model aligns with and extends current computing paradigms while offering a more flexible and transparent approach to managing complex, distributed systems. This paper aims to provide a computational foundation for implementing PnR systems, paving the way for more adaptable, analysable, and efficient cloud-native applications.
Prompt and Response, Designchunk, Response, Designchunk, Intentions, Objects, Cross Container Consistency, Input process identification, Output process Identification, Execution State, Identification, Execution State, Common Path Of Execution and Understanding, Intention Loop, Spaceloop, Intention Emission-Reflection-Absorbtion