Efficient Scheduling Algorithm for Cluster Platforms: Difference between revisions
Created page with "Title: Efficient Scheduling Algorithm for Cluster Platforms Research Question: How can we develop an efficient scheduling algorithm that optimizes both makespan and weighted minimal average completion time for jobs submitted to a cluster platform? Methodology: The researchers proposed a new scheduling algorithm called ID-IMAG, which is based on a batch policy with increasing batch sizes and smart selection of jobs in each batch. This algorithm was assessed through inte..." |
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Research Question: How can we develop an efficient scheduling algorithm that optimizes both makespan and weighted minimal average completion time for jobs submitted to a cluster platform? | Research Question: How can we develop an efficient scheduling algorithm that optimizes both makespan and weighted minimal average completion time for jobs submitted to a cluster platform? | ||
Methodology: The researchers proposed a new scheduling algorithm called ID-IMAG, which is based on a batch policy with increasing batch sizes and smart selection of jobs in each batch. This algorithm was assessed through intensive simulation results and compared to a new lower bound obtained by relaxing an | Methodology: The researchers proposed a new scheduling algorithm called ID-IMAG, which is based on a batch policy with increasing batch sizes and smart selection of jobs in each batch. This algorithm was assessed through intensive simulation results and compared to a new lower bound obtained by relaxing an ILP. | ||
Results: The ID-IMAG algorithm showed promising results in terms of both makespan and weighted minimal average completion time. | Results: The ID-IMAG algorithm showed promising results in terms of both makespan and weighted minimal average completion time. It was found to be faster than existing algorithms while maintaining a high level of performance. | ||
Implications: The ID-IMAG algorithm can be a | Implications: The ID-IMAG algorithm can be implemented in real-size cluster platforms, providing a practical solution for scheduling jobs. It offers a balance between user-oriented objectives and system administrator objectives, making it suitable for various types of jobs and applications. | ||
Link to Article: https://arxiv.org/abs/ | In conclusion, the ID-IMAG algorithm is a significant contribution to the field of scheduling algorithms for cluster platforms. It provides a fast and efficient way to optimize both makespan and weighted minimal average completion time, making it a promising solution for real-world applications. | ||
Link to Article: https://arxiv.org/abs/0405006v2 | |||
Authors: | Authors: | ||
arXiv ID: | arXiv ID: 0405006v2 | ||
[[Category:Computer Science]] | [[Category:Computer Science]] | ||
[[Category:Algorithm]] | [[Category:Algorithm]] | ||
[[Category:Scheduling]] | [[Category:Scheduling]] | ||
[[Category:It]] | |||
[[Category:Cluster]] | [[Category:Cluster]] | ||
[[Category: | [[Category:Jobs]] | ||
Revision as of 16:55, 24 December 2023
Title: Efficient Scheduling Algorithm for Cluster Platforms
Research Question: How can we develop an efficient scheduling algorithm that optimizes both makespan and weighted minimal average completion time for jobs submitted to a cluster platform?
Methodology: The researchers proposed a new scheduling algorithm called ID-IMAG, which is based on a batch policy with increasing batch sizes and smart selection of jobs in each batch. This algorithm was assessed through intensive simulation results and compared to a new lower bound obtained by relaxing an ILP.
Results: The ID-IMAG algorithm showed promising results in terms of both makespan and weighted minimal average completion time. It was found to be faster than existing algorithms while maintaining a high level of performance.
Implications: The ID-IMAG algorithm can be implemented in real-size cluster platforms, providing a practical solution for scheduling jobs. It offers a balance between user-oriented objectives and system administrator objectives, making it suitable for various types of jobs and applications.
In conclusion, the ID-IMAG algorithm is a significant contribution to the field of scheduling algorithms for cluster platforms. It provides a fast and efficient way to optimize both makespan and weighted minimal average completion time, making it a promising solution for real-world applications.
Link to Article: https://arxiv.org/abs/0405006v2 Authors: arXiv ID: 0405006v2