Efficient Scheduling Algorithm for Cluster Platforms

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