Efficient Scheduling Algorithm for Cluster Platforms: Difference between revisions
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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. | 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 | Results: The ID-IMAG algorithm showed promising results in terms of performance and efficiency. It was able to represent both user-oriented objectives and system administrator objectives, making it a versatile choice for scheduling jobs on cluster platforms. | ||
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 | 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 performance and efficiency, making it an attractive option for both users and system administrators. | ||
Link to Article: https://arxiv.org/abs/0405006v3 | |||
Link to Article: https://arxiv.org/abs/ | |||
Authors: | Authors: | ||
arXiv ID: | arXiv ID: 0405006v3 | ||
[[Category:Computer Science]] | [[Category:Computer Science]] | ||
[[Category:Algorithm]] | [[Category:Algorithm]] | ||
[[Category:Scheduling]] | [[Category:Scheduling]] | ||
[[Category:Cluster]] | [[Category:Cluster]] | ||
[[Category:Jobs]] | [[Category:Jobs]] | ||
[[Category:It]] | |||
Latest revision as of 15:55, 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 performance and efficiency. It was able to represent both user-oriented objectives and system administrator objectives, making it a versatile choice for scheduling jobs on cluster platforms.
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 performance and efficiency, making it an attractive option for both users and system administrators.
Link to Article: https://arxiv.org/abs/0405006v3 Authors: arXiv ID: 0405006v3