Abstract
Hadoop is a popular framework to process growing volumes of data across clusters of computers, and has achieved great success both in industry and academic researches. Although Hadoop has powerful batch processing capabilities, it can not support the real-time services, such as online payment or monitoring sensor data. These real-time services have strict deadlines in common, where service response after the deadline is considered useless. Current researches on time-constrained scheduling algorithms generally aim at shortening the completion time, rather than guaranteeing the specific latency for the real-time services. In this paper, we study the deadline-constrained scheduling problem on Hadoop, where service requests arrive randomly and no prior information is available. A maximum urgency scheduling MUS algorithm is proposed, and then implemented as a pluggable scheduler on Hadoop. This novel algorithm can be applied in heterogeneous environments with a low computation complexity. Experiments indicate that the MUS algorithm maximises the number of jobs meeting their deadlines while maintains the fairness among different types of jobs.
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: International Journal of Computational Science and Engineering
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.