Abstract
The goal of the proposed research is to improve the performance of Hadoop-based software running on a heterogeneous cluster. My approach lies in the intersection of machine learning, scheduling and diagnosis. We mainly focus on heterogeneous Hadoop clusters and try to improve the performance by implementing a more efficient scheduler for this class of cluster.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the AAAI Conference on Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.