Intent-based System Design and Operation

TL;DR

The paper tackles the automation gap in cloud system design, deployment, and operation by proposing intent-based self-managing clouds that encode functional and operational requirements as high-level intents. It argues that combining a Cerulean-style hierarchical generation with real-time context awareness and an ops model enables translating user intent into concrete designs, tests, and automated operations. The contributions include a structured view of intent-based cloud design, the four-task manifesting framework, and concrete use cases for distributed design, real-time comprehension, automated incident management, and metastable-failure mitigation, along with a roadmap for future research. If realized, this approach could enable autonomous cloud systems with higher productivity, reliability, and maintainability by reducing manual intervention and enabling continuous self-improvement.

Abstract

Cloud systems are the backbone of today's computing industry. Yet, these systems remain complicated to design, build, operate, and improve. All these tasks require significant manual effort by both developers and operators of these systems. To reduce this manual burden, in this paper we set forth a vision for achieving holistic automation, intent-based system design and operation. We propose intent as a new abstraction within the context of system design and operation. Intent encodes the functional and operational requirements of the system at a high-level, which can be used to automate design, implementation, operation, and evolution of systems. We detail our vision of intent-based system design, highlight its four key components, and provide a roadmap for the community to enable autonomous systems.

Figures (5)

• Figure 1: Intent-based cloud system components
• Figure 2: Functional Intent Example
• Figure 3: Operational Intent Example
• Figure 4: Real-time context awareness.
• Figure 5: System improvement with Hierarchical Generation