VM Balancing Service for Hyperscaler Resource Optimization
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Solution Overview
Problem
In hyperscale data centers, efficient utilization of data center capacity is a challenge due to manual and complex workload distribution processes, leading to idle or underutilized virtual machines (VMs) on some nodes while others are overloaded.
Innovation Solution
The implementation of software-based VM agents and node agents to collect utilization metrics, combined with a VM balancing service that analyzes these metrics to determine VM migration plans, optimizes the distribution of VMs across physical nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual workload distribution is used, then operational flexibility is maintained, but system complexity and inefficiency increase
Solution Approach 1:
The system enables automated self-service through the VM balancing service that autonomously monitors utilization metrics, identifies underutilized VMs, determines migration targets, and executes rebalancing operations without manual intervention, resolving the contradiction by replacing complex manual processes with intelligent automation
Solution Approach 2:
The patent replaces manual mechanical workload distribution with an automated software-based balancing service that uses utilization metrics and algorithms to dynamically allocate VMs, substituting human-operated mechanical processes with electronic automation to improve efficiency while reducing operational complexity
2Reliability
If VMs are over-provisioned to ensure capacity, then service reliability is improved, but energy consumption and costs increase
Solution Approach 1:
The system implements dynamic workload distribution by continuously monitoring utilization metrics and automatically rebalancing VMs across nodes based on real-time conditions, allowing the system to adapt capacity allocation dynamically rather than relying on static over-provisioning, thus maintaining reliability while reducing energy waste from idle resources
Solution Approach 2:
The VM balancing service implements feedback mechanisms by continuously collecting utilization metrics from nodes and VMs, analyzing this data to identify rebalancing opportunities, and executing migrations to optimize resource utilization, creating a closed-loop system that maintains service reliability while minimizing energy consumption through data-driven decisions
3Productivity
If VMs are concentrated on fewer nodes, then resource utilization efficiency improves, but system vulnerability to failures increases
Solution Approach 1:
The system maintains dynamic balance by continuously monitoring node health and utilization, automatically redistributing VMs to maintain both high resource utilization and adequate distribution across multiple nodes, preventing excessive concentration that would reduce fault tolerance while avoiding excessive dispersion that would lower efficiency
Data Source
AI summary
The disclosure presents techniques for optimizing the allocation of virtual machines (VMs) in a hyperscale cloud computing environment to enhance overall efficiency. The system comprises a VM agent operating within each VM environment, and a node agent running on each computing node. Both agents report utilization metrics to a central VM balancing service. This service processes the received metrics through either predefined rules or a pretrained machine learning model to evaluate VM utilization. Based on the analysis, the service identifies underutilized VMs that are candidates for migration. When a VM qualifies for migration and a suitable receiving node is available, the system generates and outputs a migration plan, aiming to improve resource utilization across the network.