Virtualized Workload Routing for Power Compliance
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Solution Overview
Problem
Current multiprocessing computer systems face challenges in dynamically managing workload and power consumption, particularly in meeting regulatory limits on CPU usage, which requires costly and error-prone manual interventions and system redesigns.
Innovation Solution
A system and method that integrates a hardware control component with a virtualization layer to manage hardware resources, allowing for dynamic routing of tasks and power management policies, enabling autonomous adjustment of resource allocation and shutdown to comply with energy efficiency and regulatory constraints without impacting system responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual monitoring and manual server quiesce/shutdown is used to manage energy consumption, then energy efficiency can be improved, but system complexity and operational cost increase significantly
Solution Approach 1:
The system implements self-service through automated agents that monitor hardware resources, evaluate policies, and execute quiesce/shutdown actions without human intervention. The workload management component autonomously responds to hardware state changes and manages task routing, eliminating the need for manual operations while reducing energy consumption.
Solution Approach 2:
The system establishes feedback loops where hardware state information is continuously monitored and fed back to the workload management component. This feedback mechanism enables dynamic adjustment of task routing and hardware utilization based on real-time conditions, optimizing energy efficiency while maintaining system responsiveness.
2Ease of operation
If automated execution of manual processes is implemented, then operational cost is reduced, but adaptability to variable demand decreases
Solution Approach 1:
The system transitions from static automated scripts to dynamic workload management that continuously adapts to changing conditions. The workload management component dynamically evaluates hardware state, task characteristics, and policy constraints to make real-time routing decisions, enabling the system to adapt to variable demand patterns while maintaining automated operation.
Solution Approach 2:
The system changes operational parameters dynamically based on hardware state and demand conditions. Task routing parameters, hardware utilization thresholds, and policy evaluations are adjusted in real-time, allowing the system to respond flexibly to variable demand while maintaining automated cost-effective operation.
3Loss of energy
If hardware resources are shut down to comply with regulatory limits, then energy efficiency improves, but system responsiveness may deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-positioning tasks on alternative hardware resources before shutdown occurs. The workload management component anticipates upcoming shutdown events and proactively routes tasks to available hardware, ensuring continuous system responsiveness while maintaining energy efficiency through planned hardware utilization.
Solution Approach 2:
The workload management component acts as an intermediary between hardware shutdown actions and task execution. It mediates the transition by managing task routing and coordination, ensuring that tasks are smoothly transferred to alternative hardware resources without impacting system responsiveness, while enabling energy-efficient hardware shutdown.
4Adaptability or versatility
If dynamic workload management over virtualization layer is implemented, then system flexibility improves, but coordination with hardware power management becomes more difficult
Solution Approach 1:
The workload management component implements multi-functionality by simultaneously handling task routing, hardware state monitoring, policy evaluation, and coordination with power management. This universal component bridges the virtualization layer and hardware layer, enabling dynamic workload management while simplifying coordination through a single integrated interface.
Data Source
AI summary
Embodiments of the invention relate to multiprocessing systems. An aspect of the invention concerns a multiprocessing system that comprises a hardware control component for selecting a hardware management action responsive to a hardware policy and a virtualization component for presenting virtual hardware resources to a software task execution environment. The system may further comprise a software workload management component for controlling at least one running software task and routing at least one new software task using the virtual hardware resources; and a communication component for signaling the software workload management component to perform a software management action in compliance with the hardware management action. The hardware policy may be a hardware power management policy, and the software management action may comprise quiescing the at least one running software task or routing the new software tasks to a different software task execution environment.