Automated Virtual Machine Migration for Data Center Power Cost Optimization
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Solution Overview
Problem
Current data center operations face inefficiencies in managing power consumption and cooling requirements due to the increasing demand of high-density server deployments, leading to high energy costs and limitations in performance, especially with the need for geographically dispersed data centers and manual server migrations.
Innovation Solution
An automated system utilizing a centralized controller and agent architecture that gathers data on various parameters to initiate server migrations between data centers based on trigger conditions, such as power costs, environmental events, and scalability needs, allowing for dynamic scaling and migration of virtual machines to optimize energy usage and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If servers are deployed in high-density configurations to increase processing capacity, then productivity is improved, but power consumption and cooling requirements increase
Solution Approach 1:
The patent implements dynamic server migration between data centers based on real-time power cost signals and energy pricing. Virtual machines are automatically moved from high-cost to low-cost data centers, enabling the system to adapt its energy consumption pattern dynamically rather than statically, thus resolving the contradiction between maintaining high processing capacity and reducing power consumption costs
Solution Approach 2:
The system changes the operational parameters of servers by migrating them between different data centers with varying power costs and energy sources. By altering the location parameter of virtual machines based on power cost differentials and renewable energy availability, the system achieves reduced energy consumption costs while maintaining overall processing capacity
2Adaptability or versatility
If manual server migration is used to manage data center operations, then adaptability is improved, but loss of time increases
Solution Approach 1:
The patent implements self-service automation where the system automatically monitors power costs, detects migration opportunities, executes server migrations, and updates configurations without human intervention. This automated self-service approach eliminates the time loss associated with manual migration while maintaining full adaptability to changing energy pricing and operational conditions
Solution Approach 2:
The system continuously monitors power cost data, energy pricing signals, and server performance metrics, using this feedback to automatically trigger and execute migrations. The feedback loop enables rapid adaptation to changing conditions without manual intervention, reducing time loss while maintaining operational flexibility
3Use of energy by stationary object
If data centers are located in areas with low electrical rates, then use of energy cost is reduced, but device complexity increases
Solution Approach 1:
The patent creates a universal automated migration system that manages servers across multiple geographically dispersed data centers. This single automated management platform performs multiple functions including monitoring power costs, making migration decisions, executing migrations, and optimizing energy consumption, thereby simplifying the complexity of managing distributed data centers while achieving low electrical rate benefits
4Productivity
If high-speed processors are deployed to increase processing power, then productivity is improved, but use of energy increases
Solution Approach 1:
The system dynamically migrates workloads between data centers based on real-time energy pricing and power cost signals. By continuously adapting the location of high-speed processors to areas with lower energy costs or renewable energy availability, the system maintains high processing speed while reducing overall power consumption expenses
Data Source
AI summary
Methods, apparatus, software, and system architectures for supporting virtualized system migrations and scaling. Under aspects of a method, data is automatically collected and aggregated at multiple levels by a plurality of agents for each of multiple data centers. The data includes data relating to virtual machine utilization, data relating to electrical utilization costs, data relating to data center utilization, and data relating to triggers events. The data is processed to determine whether to migrate virtual servers from a first data center to a second data center. The software architecture includes a plurality of modules including a controller, data center profile, transition triggers, power cost profile, and virtual machine package module. The agents are implemented in an agent hierarchy and configured to collect data themselves and/or aggregate data from other agents and provide an API to facilitate access to collected data and agent services.