Best Trade-Off Point Method for Efficient Resource Provisioning in Spark

Algorithms

PeterP. Nghiem¹ 

[1] Department of Computer Engineering, School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA;

关键词: Apache Spark;    Hadoop MapReduce;    YARN;    algorithm for best trade-off point;    optimization;    resource provisioning;    performance efficiency;    energy efficiency;    elbow curve;   

DOI  :  10.3390/a11120190

来源: DOAJ

【 摘 要 】

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.

【 授权许可】

Unknown