Processor Energy Dissipation Estimation for Thermal-Aware Task Scheduling
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Solution Overview
Problem
Data centers face significant energy inefficiencies due to the rapid growth of energy dissipation in servers and cooling systems, with traditional solutions being primarily reactive and failing to optimize energy use across computing and cooling resources effectively.
Innovation Solution
A holistic approach treating energy as a first-class resource, using fast thermal models and global schedulers to allocate energy budgets to servers proactively, combined with modified operating system kernels to manage energy consumption within budgeted limits, and dynamic cooling strategies tailored to specific thermal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If servers are overprovisioned with high capacity, then peak performance is improved, but energy efficiency deteriorates due to operation at low utilization levels
Solution Approach 1:
The patent implements dynamic workload scheduling that actively monitors and adjusts server utilization levels in real-time, transitioning servers between active and standby states based on current workload demands, thereby optimizing energy efficiency while maintaining peak performance capability
Solution Approach 2:
The system performs preliminary thermal analysis and workload prediction to proactively schedule tasks before thermal conditions deteriorate, preventing thermal emergencies before they occur and allowing servers to operate at optimal energy efficiency points
2Productivity
If more servers are packed into data centers, then capacity is improved, but energy dissipation and cooling requirements worsen
Solution Approach 1:
The patent implements rack-level thermal management that treats each rack as an independent thermal zone with localized monitoring and control, allowing differential cooling strategies and workload scheduling per rack to reduce overall energy dissipation while maintaining high capacity
Solution Approach 2:
The system employs continuous thermal monitoring and feedback loops that sense temperature conditions and automatically adjust workload scheduling and cooling resource allocation, creating a closed-loop control system that optimizes energy efficiency at scale
3Ease of operation
If traditional reactive cooling solutions are used, then response to thermal conditions is simple, but energy optimization across computing and cooling resources deteriorates
Solution Approach 1:
The patent merges computing workload management with thermal and cooling resource management into a unified scheduling system, where task allocation decisions simultaneously optimize computational efficiency and thermal conditions, achieving overall energy optimization while maintaining operational simplicity
Data Source
AI summary
A system and method of scheduling tasks, comprising receiving activity and performance data from registers or storage locations maintained by hardware and an operating system; storing calibration coefficients associated with the activity and performance data; computing an energy dissipation rate based on at least the activity and performance data; and scheduling tasks under the operating system based on the computed energy dissipation rate.