TEA Scheduling for Energy-Aware Data Maintenance and Deletion
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Solution Overview
Problem
Existing data maintenance and deletion processes in computing systems do not consider energy-related factors, leading to inefficient energy consumption and lack of cost optimization.
Innovation Solution
Implementing a trustworthy energy awareness (TEA) module that uses machine learning to determine optimal time slots for data maintenance and deletion based on energy consumption patterns and user constraints, ensuring energy-efficient operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data maintenance and deletion processes are performed continuously or frequently, then data quality and reliability are improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the timing and frequency of data maintenance and deletion operations based on real-time energy cost signals and renewable energy availability. The TEA module continuously monitors energy prices and schedules maintenance tasks during periods of lower energy costs or higher renewable energy availability, transforming static maintenance schedules into dynamic, adaptive operations that balance data quality requirements with energy consumption concerns.
Solution Approach 2:
The invention changes the temporal parameter of maintenance operations by scheduling them at specific times based on energy cost parameters and renewable energy availability. Instead of performing maintenance at fixed intervals or continuously, the system modifies the timing parameter dynamically according to energy market conditions, thereby reducing overall energy consumption while maintaining data quality through appropriately timed maintenance activities.
2Use of energy by moving object
If data maintenance operations are delayed to reduce energy consumption, then energy costs are reduced, but data quality and availability may deteriorate
Solution Approach 1:
The TEA module implements a feedback mechanism that continuously monitors energy cost signals, renewable energy availability, and system state to make intelligent scheduling decisions. The system receives feedback about current energy prices and availability conditions, processes this information through machine learning models, and adjusts maintenance scheduling accordingly. This feedback loop ensures that data maintenance is delayed only when energy conditions are unfavorable while maintaining data quality standards, preventing deterioration of data availability.
Solution Approach 2:
The system performs preliminary assessment of energy conditions and data state before scheduling maintenance operations. The TEA module evaluates current energy costs, renewable energy forecasts, and data quality metrics in advance to determine optimal maintenance timing. This preliminary action allows the system to proactively schedule maintenance during favorable energy conditions while ensuring data quality requirements are met, rather than reactively responding to energy price fluctuations.
3Ease of operation
If conventional garbage collection is performed periodically based on memory thresholds, then memory management is simplified, but energy efficiency is compromised
Solution Approach 1:
The invention transforms static, threshold-based garbage collection scheduling into dynamic, energy-aware scheduling. Instead of performing garbage collection whenever memory usage exceeds a fixed threshold, the system dynamically adjusts collection timing based on real-time energy cost signals and renewable energy availability. The TEA module integrates memory management requirements with energy optimization goals, creating adaptive garbage collection schedules that maintain ease of operation while significantly improving energy efficiency through intelligent timing decisions.
Data Source
AI summary
One example method includes receiving a request to perform a data process, determining, by a trustworthy energy awareness (TEA) module, based on energy information and user constraints, a best time to perform the data process, and conveying, by the TEA module to a recipient, information indicating the best time to perform the data process. The data process may be a data backup process, or a garbage collection process, for example.