An empirical analysis of scheduling techniques for real-time cloud-based data processing

• Published in: 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA) pp. 1 - 8
• Main Authors: Phan, L. T. X., Zhuoyao Zhang, Qi Zheng, Boon Thau Loo, Insup Lee
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2011
• Subjects: Data processing; Processor scheduling; Real time systems; Schedules; Scheduling; Timing
• ISBN: 9781467303187; 1467303186
• ISSN: 2163-2871; 2689-7121
• DOI: 10.1109/SOCA.2011.6166240

In this paper, we explore the challenges and needs of current cloud infrastructures, to better support cloud-based data-intensive applications that are not only latency-sensitive but also require strong timing guarantees. These applications have strict deadlines (e.g., to perform time-dependent mission critical tasks or to complete real-time control decisions using a human-in-the-loop), and deadline misses are undesirable. To highlight the challenges in this space, we provide a case study of the online scheduling of MapReduce jobs executed by Hadoop. Our evaluations on Amazon EC2 show that the existing Hadoop scheduler is ill-equipped to handle jobs with deadlines. However, by adapting existing multiprocessor scheduling techniques for the cloud environment, we observe significant performance improvements in minimizing missed deadlines and tardiness. Based on our case study, we discuss a range of challenges in this domain posed by virtualization and scale, and propose our research agenda centered around the application of advanced real-time scheduling techniques in the cloud environment.