Scheduling Spark Tasks With Data Skew and Deadline Constraints

Data skew has an essential impact on the performance of big data processing. Spark task scheduling with data skew and deadline constraints is considered to minimize the total rental cost in this paper. A modified scheduling architecture is developed in terms of the unique characteristics of the considered problem. A mathematical model is constructed, and a Spark task scheduling algorithm is proposed considering both the data skew and deadline constraints. The algorithm consists of three components: stage sequencing, task scheduling, and scheduling adjustment. Strategies for each of the components are presented. The parameters and components of the proposed algorithm are calibrated over many random instances. The calibrated algorithm is compared to two existing algorithms for similar problems over classical scientific workflow applications. Experimental results show that the proposed algorithm outperforms the compared algorithms statistically.

Automated summary

This paper investigates Spark task scheduling with data skew and deadline constraints, and proposes an optimized algorithm which outperforms existing algorithms in big data processing performance based on experimental results.