Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems
Abstract
1. Introduction
- We propose a novel search-based optimal energy frequency adjustment algorithm (SEFFA). Predicated on the assumption that all nodes are pre-allocated, this algorithm strives to minimize energy consumption while fulfilling reliability prerequisites through strategic frequency adjustments.
- We introduce an energy-aware scheduling algorithm based on the weight method under reliability constraints (EAWRS). This methodology integrates both the out-degree and in-degree of the DAG and employs a combination of task completion times, energy consumption metrics, and reliability factors, expressed through normalized and linear combinations, to optimize node allocation.
- The efficacy of the proposed algorithms is validated through comprehensive experimental simulations. The findings demonstrate that these methods outperform existing techniques in terms of energy consumption and makespan.
2. Related Work
3. Models
3.1. System Model
3.2. Energy Model
3.3. Reliability Model
3.4. Problem Description
4. Energy-Aware Weighted Scheduling Algorithm with Reliability Constraints
4.1. The Task Priority Ordering Phase
4.2. The Processor Allocation Phase
Algorithm 1: EAWRS | |
Input: , Output: | |
1: | Calculate of all tasks |
2: | Calculate according to Equation (14) |
3: | Push valuese into queue |
4: | Push values into queue in decreasing order |
5: | for (∀,) do |
6: | if then |
7: | Assign to processor satisfying: |
8: | |
9: | else |
10: | Assign to processor satisfying: |
11: | |
12: | end if |
13: | end for |
14: | return ) |
- Calculate the value for each task.
- Place the calculated IOE values in queue in decreasing order.
- If task is within the range of , it indicates a high criticality level, and it is then assigned to processor with
- If task is within the range of , it indicates a lower criticality level, and it should then be assigned to processor with .
- Calculate the upward rank values () for each task and assign tasks to the appropriate processors according to Formulas (14) and (15). The final result is a scheduling solution.
5. Search-Based Optimal Energy Frequency Adjustment Algorithm
Algorithm 2: SEFFA | |
Input: Output: | |
1: | Initialize Fibonacci sequence with sufficient length |
2: | Compute the upper and lower bounds of the search region and |
3: | while do |
4: | = length_of_ |
5: | Compute and |
6: | for each processor do: |
7: | = , = |
8: | while ( − > ) do |
9: | = ( + /2 |
10: | if < then |
11: | = |
12: | else |
13: | = |
14: | end if |
15: | end while |
16: | for each task allocated to the processor do |
17: | = |
18: | end for |
19: | Compute |
20: | if < then |
21: | = |
22: | else |
23: | = |
24: | end if |
25: | Update for next iteration |
26: | end while |
27: | return |
6. Experiments
6.1. Comparative Algorithms
6.2. Experimental Platform and DAG Applications
6.3. Evaluation Metrics
6.4. Results and Analysis
6.5. Key Limitations
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Notation | Definition |
---|---|
Execution time of task in processor | |
The edge between task and task | |
Communication time from to | |
Average execution time of task | |
Dynamic power consumption independent of processor frequency | |
Static energy consumption | |
System state | |
Power consumption related to processor frequency | |
The effective capacitance of processor | |
Dynamic power index of processor | |
Energy consumption of application | |
Reliability value of task in processor | |
Reliability value of application | |
Upward rank value of task | |
The successor to task | |
The finish time of executing task on processor | |
The minimum values of finish time of executing task | |
The maximum values of finish time of executing task | |
Reliability requirement of application | |
Reliability value of task under the minimum redundancy of the task | |
Reliability value of task under the maximum redundancy of the task | |
The execution time of sub-task on a processor with its maximum frequency | |
Failure rate of processor |
Task | |||
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Chen, Z.; Wu, J.; Cheng, L.; Tao, T. Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems. Big Data Cogn. Comput. 2025, 9, 160. https://doi.org/10.3390/bdcc9060160
Chen Z, Wu J, Cheng L, Tao T. Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems. Big Data and Cognitive Computing. 2025; 9(6):160. https://doi.org/10.3390/bdcc9060160
Chicago/Turabian StyleChen, Ziyu, Jing Wu, Lin Cheng, and Tao Tao. 2025. "Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems" Big Data and Cognitive Computing 9, no. 6: 160. https://doi.org/10.3390/bdcc9060160
APA StyleChen, Z., Wu, J., Cheng, L., & Tao, T. (2025). Research on High-Reliability Energy-Aware Scheduling Strategy for Heterogeneous Distributed Systems. Big Data and Cognitive Computing, 9(6), 160. https://doi.org/10.3390/bdcc9060160