Data-Centric Task Scheduling Algorithm for Hybrid Tasks in Cloud Data Centers

• Published in: Algorithms and Architectures for Parallel Processing Vol. 11335; pp. 630 - 644
• Main Authors: Li, Xin, Wang, Liangyuan, Abawajy, Jemal, Qin, Xiaolin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Batched task; Data analysis; Data migration; Hybrid scheduling; Real-time task
• ISBN: 9783030050535; 303005053X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-05054-2_47

With the development of big data, a demand for data analysis keeps increasing. This requirement has prompted a need for data-aware task scheduling approach that can simultaneously schedule various tasks such as batched tasks and real-time tasks in a data center efficiently. To this end, we propose a hybrid task scheduling strategy coupled with data migration in data center. Firstly, we translate the task scheduling problem into task selection problem, and give methods of selecting batched tasks and real-time tasks respectively. Then the method for scheduling both batched tasks and real-time tasks is introduced in detail. Finally, we integrate data migration into the hybrid scheduling strategy. Experimental results show that, compared to the traditional FIFO algorithm, the proposed task scheduling strategy greatly improves the data locality and data migration performs very well on reducing the job execution time. Our algorithm also guarantees an acceptable fairness for tasks.