Data Center Task Scheduling for Flexible Energy-Intelligent Computing
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Solution Overview
Problem
The demand for computational resources is outpacing the supply of infrastructure and sustainable energy sources, leading to bottlenecks in data center construction and limiting flexibility in energy use, which hampers data centers' ability to participate in energy incentives and reduce costs.
Innovation Solution
A system and method for managing computing tasks that involves a controller to receive task requests, present execution factors, select datacenters based on demand and supply strategies, and adjust energy consumption to optimize task scheduling and revenue generation through energy agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data centers operate as rigid loads maintaining baseline electricity consumption to ensure redundancy and uptime, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic load scheduling that allows data centers to transition from rigid baseline operation to flexible task scheduling. The system dynamically adjusts computing task execution timing and energy consumption patterns while maintaining service level agreements, enabling participation in energy incentive programs without compromising reliability.
Solution Approach 2:
The system changes operational parameters by allowing variable electricity consumption patterns instead of fixed baseline loads. It modifies task scheduling parameters to align with energy incentive programs, changing when and how computing tasks are executed to capture energy incentives while maintaining required uptime through coordinated task rescheduling.
2Reliability
If data centers adopt consistent strategies centered on maintaining redundancy and uptime, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent replaces consistent rigid strategies with dynamic adaptive strategies. The system continuously monitors energy incentive programs and automatically adjusts computing task schedules to capitalize on incentive opportunities, enabling data centers to pursue advanced energy strategies while maintaining reliability through real-time task redistribution and execution timing adjustments.
3Reliability
If new data centers are constructed adhering to high standards (tier 4), then reliability is improved, but loss of time and loss of substance worsen
Solution Approach 1:
The patent changes the reliability assurance approach from construction-phase hardware redundancy to operational-phase software-based load scheduling. Instead of requiring tier 4 construction standards with extensive physical redundancy, the system achieves equivalent reliability through dynamic task scheduling and coordination, dramatically reducing construction time and infrastructure costs.
4Reliability
If new data centers are constructed adhering to high standards (tier 4), then reliability is improved, but loss of substance worsens
Solution Approach 1:
The patent transforms the reliability investment from capital-intensive construction (tier 4 standards) to operational-intensive software scheduling. The system achieves the same reliability outcome through intelligent task scheduling and energy management, eliminating the need for expensive physical infrastructure upgrades and reducing construction costs significantly.
5Productivity
If infrastructure expansion is pursued to meet growing computing demand, then productivity is improved, but loss of substance worsens
Solution Approach 1:
The patent makes existing data center infrastructure multi-functional by enabling participation in energy incentive programs. Instead of building new infrastructure to meet demand, the system extracts additional value from existing facilities through energy market engagement, allowing current infrastructure to serve both computing delivery and energy incentive capture functions simultaneously.
Solution Approach 2:
The system changes the productivity enhancement approach from infrastructure expansion to operational optimization. By modifying task scheduling parameters and energy consumption patterns, the system achieves increased effective productivity through energy incentive revenues without requiring additional physical infrastructure investment.
Data Source
AI summary
Systems and methods for managing a computing task are provided. The system includes a controller configured to receive the computing task, present to a client a set of factors defining desired conditions related to executing the computing task, receive a demand computing strategy associated with the computing task indicating one or more factors selected by the client and a constraint and a weight related to each factor from the one or more selected factors, receive a plurality of supply computing strategies from a plurality of datacenters, each supply computing strategy corresponding to a datacenter from the plurality of datacenters, calculate a task scheduling strategy based at least on the demand computing strategy and a supply computing strategy, select a candidate datacenter from the plurality of datacenters according to a predetermined rule, and schedule the computing task for execution on the candidate datacenter according to the task scheduling strategy.