Power-Aware Computing Cluster Ranking for Workload Migration
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Solution Overview
Problem
Existing data center systems lack efficient methods for managing computing workloads to ensure high availability and reliability, particularly in the face of power disruptions and varying computational demands across different geo-locations.
Innovation Solution
A method and system for managing computing workloads by identifying and ranking computing clusters based on telemetry data, computational processing loads, geo-location costs, and power device health, enabling intelligent workload migration to maintain high availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing workloads are distributed across multiple computing clusters to ensure high availability, then service reliability is improved, but system complexity increases due to the need for workload management and migration capabilities
Solution Approach 1:
The patent introduces a workload management system that acts as an intermediary between computing clusters and workloads. This system monitors cluster health, power device status, and computational loads, then automatically makes decisions about workload placement and migration. By centralizing these management functions, the system reduces the complexity that would otherwise be distributed across multiple clusters while maintaining high availability through intelligent workload distribution.
Solution Approach 2:
The system implements continuous feedback loops by monitoring telemetry data from power devices, computing nodes, and storage devices across all clusters. This feedback mechanism provides real-time information about cluster health and resource availability, enabling the workload management system to dynamically adjust workload placement decisions. The feedback-driven approach automates complex decisions, reducing manual management complexity while ensuring service reliability through responsive workload migration.
2Duration of action of stationary object
If workload migration capabilities are implemented to handle power disruptions, then service continuity is improved, but response time increases due to the overhead of monitoring and decision-making processes
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-assessing the health status of power devices, computing nodes, and storage devices before disruptions occur. Telemetry data is collected and analyzed in advance, allowing the workload management system to identify vulnerable clusters and pre-plan migration paths. When a power disruption occurs, workloads can be migrated more quickly because the decision-making framework and target cluster selections have already been prepared.
Solution Approach 2:
The workload management system implements dynamic monitoring and decision-making that adapts to changing conditions in real-time. Rather than using static, pre-configured migration rules, the system continuously adjusts workload placement decisions based on current telemetry data, power device health status, and cluster computational loads. This dynamic approach optimizes response time by making intelligent decisions based on current system state rather than following rigid predetermined paths.
3Measurement precision
If comprehensive telemetry data collection is implemented across all data center elements, then monitoring accuracy is improved, but data processing load increases
Solution Approach 1:
The system extracts only the most critical telemetry data points from the comprehensive set of available measurements. Rather than processing all collected data equally, the workload management system identifies and focuses on key indicators such as power device health status, computational load thresholds, and storage device operational state. This selective extraction approach maintains monitoring accuracy for critical parameters while reducing the overall data processing load and associated energy consumption.
Solution Approach 2:
The system applies different levels of monitoring intensity and data collection frequency to different data center elements based on their criticality and role. Critical components such as power devices and computing nodes generate detailed telemetry data that is continuously monitored, while less critical elements are monitored at lower frequencies or with reduced detail. This localized quality approach ensures accurate monitoring where needed while minimizing unnecessary data processing and energy consumption in less critical areas.
Data Source
AI summary
Managing computing workloads within a computing environment including identifying computing parameters of datacenter elements of each computing cluster of a computing environment; for each computing cluster of the computing environment: determining a health of the power device of the computing cluster; for each computing node of the computing cluster: determining a processing load of the computing node; determining a computing cost associated with a geo-location of the computing node; calculating, for each computing cluster, an availability of computing resources of the computing cluster based on the computing parameters of the data center elements of the computing cluster, the health of the power device of the computing cluster, the processing load of each computing node of the computing cluster, and the computing cost of each computing node of the computing cluster; generating a ranking of each computing cluster based on the availability of the computing resources of the computing cluster.