LAS: Logical-Block Affinity Scheduling In Big Data Analytics Systems

Authors:
Liang Bao XiDian University, P.R. China
Chase Q Wu New Jersey Institute of Technology & Oak Ridge National Laboratory, USA
Qi Haiyang Xidian University, P.R. China
Weizhao Chen Xidian University, P.R. China
Xin Zhang XiDian University, P.R. China
Weina Han XiDian University, P.R. China
Wei Wei Xidian University, P.R. China
En Tai Xidian University, P.R. China
Hao Wang Xidian University, P.R. China
Jiahao Zhai Xidian, P.R. China
Xiang Chen Xidian University, P.R. China

Abstract:

Parallel computing combined with distributed data storage and management has been widely adopted by most big data analytics systems. Scheduling computing tasks to improve data locality is crucial to the performance of such systems. While existing schedulers target near-data scheduling on top of physical data blocks, these systems face a new scheduling problem where computing tasks process table-based datasets directly and access large physical blocks indirectly through their indices stored in associated small logical blocks. This new problem invalidates the basic assumption made by many existing algorithms on near-data scheduling. In this paper, we propose a Logical-block Affinity Scheduling (LAS) algorithm to coordinate the near-data scheduling of computing tasks and the placement of logical blocks for a desired balance between data-locality and load-balancing to maximize system throughput. The proposed algorithm is implemented and evaluated using a well-known big data benchmark and a practical production system deployed in public clouds. Extensive experimental results illustrate the performance superiority of LAS over three existing scheduling algorithms.

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