Datacenter Workload Migration via Efficiency Ranking
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Solution Overview
Problem
Conventional server cluster managers in datacenters deploy workloads inefficiently, leading to excessive power consumption and increased operational costs due to random workload distribution across server devices, which can result in inefficient use of power resources.
Innovation Solution
A datacenter efficiency management system that generates node group rankings based on power consumption and performance, groups node devices into homogeneous subsets, deploys workloads optimally within these groups, and migrates workloads between nodes to ensure efficient power usage and performance alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If workloads are randomly deployed on server devices within a cluster, then deployment simplicity is maintained, but power consumption efficiency deteriorates
Solution Approach 1:
The system pre-calculates and stores efficiency rankings for each node device based on power consumption and performance metrics before workload deployment. This preliminary action enables the workload manager to make informed deployment decisions without complex real-time calculations, maintaining operational simplicity while improving energy efficiency.
Solution Approach 2:
The patent replaces random workload deployment (mechanical/simple assignment) with an intelligent ranking-based system that uses calculated efficiency metrics. The node group ranking and workload performance efficiency ranking systems substitute the random assignment mechanism with a data-driven selection process that optimizes power consumption while maintaining automated operation.
2Productivity
If workloads are deployed on high-performance node devices, then workload performance is improved, but power consumption increases
Solution Approach 1:
The system creates different node groups with distinct quality characteristics based on their power consumption and performance profiles. High-efficiency node groups are identified and designated for specific workload types, while low-efficiency groups are used differently. This local quality differentiation allows workloads to be placed on nodes with appropriate performance-to-power ratios rather than uniformly using high-performance nodes.
Solution Approach 2:
The patent changes the selection parameter from pure performance metrics to a composite ranking that includes both power consumption and performance. By introducing efficiency rankings as the selection criterion, the system transforms the deployment decision from performance-only optimization to a balanced optimization that considers both productivity and energy consumption.
3Productivity
If node devices are grouped into homogeneous subsets, then workload deployment efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the datacenter's node devices into distinct node groups based on efficiency rankings. This segmentation creates manageable subsets of nodes with similar characteristics, allowing the workload manager to make targeted deployment decisions for each group. The segmentation automates what would otherwise be complex manual categorization, reducing operational complexity while improving deployment efficiency.
Solution Approach 2:
The efficiency ranking system acts as an intermediary layer between the raw node device characteristics and the workload deployment decisions. Rather than directly managing complex node configurations, the system uses the calculated rankings as an intermediate metric that simplifies the decision-making process. This intermediary ranking mechanism translates complex multi-parameter node characteristics into a single comparable metric.
Data Source
AI summary
A datacenter includes a datacenter efficiency management system coupled to node devices. For each of the node devices and based on a power consumption associated with that node device and a performance associated with that node device, the datacenter efficiency management system generates a node group ranking that it uses to group subsets of the node devices into respective homogenous node groups, and then deploys a respective workload on at least one node device in each of the homogenous node groups. Based on at least one of a node workload bandwidth, a node power consumption, and a node health of each node device on which a workload was deployed, the datacenter efficiency management system then generates a workload performance efficiency ranking of the node devices that it then uses to migrate at least one workload between the node devices.