Intent-Based Workload Orchestration Across Heterogeneous Compute
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Solution Overview
Problem
Current orchestration solutions for edge computing systems face challenges such as vendor-lock, incorrect resource allocation, and sub-optimal performance due to the lack of contextual information in quality of service (QoS) management, especially in heterogeneous compute environments, leading to overprovisioning and increased costs.
Innovation Solution
Implementing intent-based orchestration that maps service level objectives (SLOs) and key performance indicators (KPIs) using a meta-language to express dynamic requirements, allowing for nested and graduated Service Level Agreements (SLAs) that adapt to bursts and optimize resource use, while abstracting users from detailed resource specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional orchestration solutions are used in heterogeneous compute environments, then vendor-specific implementations can be deployed, but vendor-lock occurs and adaptability to different compute platforms is reduced
Solution Approach 1:
The patent implements a universal orchestration framework that can manage diverse compute platforms (CPU, GPU, FPGA, ASIC) through a common interface. The system uses platform-agnostic resource abstraction layers that enable the same orchestration logic to operate across different hardware types, eliminating vendor-lock while maintaining ease of deployment through standardized workflows.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that sits between the control plane and heterogeneous compute resources. This mediator translates high-level service level objectives into platform-specific configurations, enabling adaptability to different compute platforms without requiring changes to the core orchestration logic or deployment processes.
2Measurement precision
If detailed resource specifications are provided in QoS requests, then resource allocation can be precise, but complexity of orchestration increases and contextual information is lost
Solution Approach 1:
The patent segments the orchestration process into distinct layers: a control plane that handles high-level service level objectives (SLOs) and key performance indicators (KPIs), and a data plane that executes specific resource allocation tasks. This segmentation allows precise resource allocation to be achieved through coordinated actions across multiple specialized components rather than a single complex orchestration system.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that sits between the control plane and heterogeneous compute resources. This mediator translates high-level service level objectives into platform-specific configurations, enabling adaptability to different compute platforms without requiring changes to the core orchestration logic or deployment processes.
3Ease of manufacture
If static resource provisioning is used, then implementation is simple, but performance is sub-optimal during traffic bursts and overprovisioning occurs
Solution Approach 1:
The patent implements dynamic resource provisioning that adapts to changing traffic conditions in real-time. The system continuously monitors actual resource utilization and adjusts allocations based on observed patterns, enabling optimal performance during traffic bursts while avoiding overprovisioning during low-utilization periods. This dynamic approach maintains simplicity through automated feedback loops rather than complex manual configuration.
Solution Approach 2:
The patent implements feedback mechanisms where the orchestration system continuously monitors actual resource utilization and performance metrics, then uses this information to adjust future resource allocations. This closed-loop control enables the system to learn from past behavior and optimize resource provisioning dynamically, achieving high productivity while maintaining implementation simplicity through automated adaptation.
4Measurement precision
If contextual information is included in QoS management, then resource allocation accuracy improves, but complexity of information processing increases
Solution Approach 1:
The patent segments information processing by separating contextual information collection (performed by lightweight agents on compute nodes) from complex analysis (performed by the orchestration system). This segmentation allows accurate resource allocation based on comprehensive contextual information while minimizing information processing overhead at any single point in the system.
Data Source
AI summary
Various systems and methods for implementing intent-based orchestration in heterogenous compute platforms are described herein. An orchestration system is configured to: receive, at the orchestration system, a workload request for a workload, the workload request including an intent-based service level objective (SLO); generate rules for resource allocation based on the workload request; generate a deployment plan using the rules for resource allocation and the intent-based SLO; deploy the workload using the deployment plan; monitor performance of the workload using real-time telemetry; and modify the rules for resource allocation and the deployment plan based on the real-time telemetry.