Assurance Intent Mapping for Edge Workload Orchestration
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Solution Overview
Problem
Edge computing environments face challenges in resource orchestration and management, particularly in mapping assurance intents to resource allocation, managing risk, and ensuring timely and efficient workload execution due to limitations in existing monitoring and analytics stacks, which lead to suboptimal performance and increased latency.
Innovation Solution
The implementation of a system that uses machine-readable instructions and programmable circuitry to automate the mapping of assurance intents to resource orchestration, perform risk assessments, and dynamically allocate resources through forced reservation and scaling, enabling proactive risk mitigation and efficient workload management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring and analytics stacks are used for resource orchestration, then system simplicity is maintained, but service level agreement compliance and workload execution efficiency deteriorate
Solution Approach 1:
The patent segments the monitoring and analytics functionality into modular components including intent translators, risk assessors, and resource orchestrators. Each component handles specific aspects of assurance intent mapping, allowing the system to achieve high reliability through specialized functional modules while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer consisting of intent translators and assurance intent mapping components that bridge the gap between high-level service level agreements and low-level resource orchestration. This intermediary layer improves compliance by systematically translating intents into actionable resource allocation decisions without requiring complete system redesign.
2Productivity
If manual resource allocation methods are used, then system complexity is reduced, but workload execution time and latency increase
Solution Approach 1:
The patent implements preliminary risk assessment and resource reservation mechanisms that proactively prepare resource allocation before workloads are executed. By performing risk assessments and mapping assurance intents in advance, the system reduces execution time and latency while managing complexity through automated preprocessing steps.
Solution Approach 2:
The patent enables self-service resource orchestration where the system automatically translates assurance intents into resource allocation decisions without manual intervention. The intent translators and risk assessors autonomously map service level agreements to resource requirements, improving execution efficiency while the modular architecture keeps complexity manageable.
3Adaptability or versatility
If dynamic resource allocation is implemented, then resource utilization improves, but system control complexity increases
Solution Approach 1:
The patent utilizes parameter changes in assurance intent mapping to dynamically adjust resource allocation. By translating varying service level agreement parameters into corresponding resource requirements, the system achieves high adaptability while the structured mapping process maintains control complexity at manageable levels.
Solution Approach 2:
The patent implements dynamic resource orchestration where the system continuously adapts resource allocation based on changing workload requirements and service level agreements. The intent translators and risk assessors dynamically map new intents to appropriate resources, providing versatility while the automated mapping mechanisms keep control complexity manageable.
Data Source
AI summary
Methods, apparatus, and systems are disclosed for mapping active assurance intents to resource orchestration and life cycle management. An example apparatus disclosed herein is to reserve a probe on a compute device in a cluster of compute devices based on a request to satisfy a resource availability criterion associated with a resource of the cluster, apply a risk mitigation operation based on the resource availability criterion before deployment of a workload to the cluster, and monitor whether the criterion is satisfied based on data from the probe after deployment of the workload to the cluster.