VM Host Migration for Data Center Power Reduction
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Solution Overview
Problem
Large information technology (IT) infrastructures, such as data centers, face significant power and cooling costs due to underutilized computing resources, with existing solutions like Energy Star compliant devices and CPU power throttling offering modest savings as current leakage remains a challenge.
Innovation Solution
The implementation of software utilization agents and virtual machine (VM) hosts within a data center to dynamically regulate power consumption by migrating workload and powering down underutilized compute nodes, leveraging machine virtualization to optimize resource usage and reduce energy waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are kept active to meet potential workload demands, then service availability is improved, but power consumption increases due to idle resources
Solution Approach 1:
The system dynamically adjusts the power state of computing resources based on real-time workload monitoring. Compute nodes transition between active and powered-off states according to demand, making the system's power consumption adaptive rather than static. This resolves the contradiction by ensuring resources are active only when needed for availability while powering down when idle to reduce consumption.
Solution Approach 2:
The system uses automated agents to monitor workload and manage power states without manual intervention. The self-service mechanism continuously assesses resource utilization and autonomously decisions which compute nodes should be active or powered down, balancing availability requirements with energy savings through intelligent automation.
2Use of energy by moving object
If computing resources are powered down to save energy, then power consumption is reduced, but service availability deteriorates when workload demand increases
Solution Approach 1:
The system performs preliminary actions by maintaining a pool of compute nodes in a powered-off state that can be rapidly activated when workload demands increase. Virtual machine migration capabilities are pre-established, allowing quick redistribution of workloads to available nodes, thus maintaining service availability while enabling energy savings during low-demand periods.
Solution Approach 2:
The system implements continuous feedback loops where agents monitor workload metrics and communicate with power management components. This feedback mechanism ensures that when workload thresholds are exceeded, the system responds by activating additional compute nodes, thereby maintaining service availability while optimizing power consumption based on actual demand conditions.
3Use of energy by moving object
If CPU power throttling is applied to reduce power consumption, then energy savings are achieved, but current leakage losses remain significant
Solution Approach 1:
The system extracts the fundamental problem by completely powering off compute nodes rather than merely throttling CPUs. This extraction approach removes the source of current leakage entirely by eliminating power to the entire node, achieving superior energy savings compared to partial throttling methods that leave leakage currents active.
Solution Approach 2:
The system changes the power state parameter from partial throttling to complete power-off states. By transitioning compute nodes between distinct power states (fully active vs. completely powered down) rather than using intermediate throttling levels, the system achieves more effective energy savings that eliminate current leakage losses associated with partially powered components.
4Adaptability or versatility
If spare computing resources are maintained for instant capacity on demand, then service scalability is improved, but power consumption increases as resources wait to be called upon
Solution Approach 1:
The system implements dynamic resource allocation where the computing capacity available for instant demand is not fixed but adapts based on current workload and power state of nodes. Virtual machine migration capabilities enable the system to dynamically activate previously powered-down nodes when demand arises, providing scalability without requiring permanently active spare resources, thus reducing their idle power consumption.
Data Source
AI summary
A system for dynamically regulating power consumption in an information technology (IT) infrastructure having a plurality of compute nodes interconnected over a network is provided. The system includes at least one virtual machine (VM) host deployed at each of the plurality of compute nodes, the at least one VM host is operable to host at least one VM guest, and the VM hosts on different ones of the plurality of compute nodes are version compatible to enable migration of the VM guests among the VM hosts. The system further includes a management module connected to the plurality of compute nodes over the network to receive a native measurement of a performance metric of a computing resource in each of the plurality of compute nodes, the management module is operable to dynamically regulate power consumption of the plurality of compute nodes by migrating the VM guests among the VM hosts based at least on the received performance metrics of the plurality of compute nodes.