Workload Placement via Observability Matching
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Solution Overview
Problem
Current workload monitoring and resource allocation in cloud networks face inefficiencies due to universal observability technologies that do not account for varying observability resources across computing resources, leading to suboptimal workload placement and performance.
Innovation Solution
A workload orchestrator that allocates and deploys workloads based on user-requested metrics, availability scores, and intent-based descriptions to match workloads with suitable observability resources, optimizing compute, storage, and network observability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If universal observability technologies are used across all computing resources, then observability coverage is improved, but resource complexity and cost increase
Solution Approach 1:
The patent implements local quality by assigning different observability capabilities to different computing resources based on their specific roles and requirements. Instead of uniformly equipping all resources with complete observability instrumentation, the system selectively applies observability features where needed, matching the complexity of observability resources to the actual monitoring requirements of each workload type.
Solution Approach 2:
The workload orchestrator serves as a universal coordinating entity that manages multiple computing resources with diverse observability capabilities. It provides a unified interface for workload placement decisions while handling the complexity of matching various workload requirements with available observability resources across the heterogeneous infrastructure.
2Productivity
If workload placement is optimized based on detailed observability metrics, then workload placement efficiency is improved, but measurement and detection complexity increases
Solution Approach 1:
The workload orchestrator acts as an intermediary between workloads and computing resources, handling the complex task of matching observability requirements with available resources. It translates high-level workload placement criteria into specific resource selections, abstracting away the complexity of individual metric collection and analysis from both the workloads and the underlying infrastructure.
Solution Approach 2:
The system performs preliminary characterization of computing resources, pre-identifying which resources possess which observability capabilities. This advance preparation allows the workload orchestrator to make efficient placement decisions without requiring complex real-time analysis, as the matching criteria are already established before workload placement occurs.
3Measurement precision
If instrumentation is deployed on all computing resources for detailed observability, then monitoring precision is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent applies local quality by deploying instrumentation and observability resources only on specific computing resources where they are actually needed for particular workload types. This selective approach avoids the overhead of universal instrumentation, maintaining high monitoring precision for critical resources while preserving resource utilization efficiency across the overall system.
Data Source
AI summary
Techniques are described for using observability to allocate and deploy workloads for execution by computing resources in a cloud network. The workloads may be allocated and deployed to the computing resources based on metrics. The workloads may be deployed to the computing resources, based on the computing resources providing a number of types of observability that matches the number of metrics. The workloads may be deployed to the computing resources, further based on each of the computing resources matching a corresponding one of the metrics. Deployment of the workloads may be further based on availability of the computing resources. The workloads may be redeployed to other computing resources that provide different types of observability associated with the metrics, in comparison to the initial computing resources. The workloads may be allocated and deployed based on intent based descriptions indicating characteristics utilized to determine types of metrics for providing observability.