中科院数学与系统科学研究院
数学研究所
学术报告会
报告人：Dr. Farshid Farhat(State University of Pennsylvania)
题 目：Stochastic Modeling and Optimization of MapReduce Framework
时 间：2016.05.16（星期一），10:00--11:00
地 点：数学院南楼913室
Abstract:
MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This paper analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.
Bio:
Farshid Farhat received the BS/MS/PhD degrees in electrical engineering at Sharif University of Technology before joining PhD program in computer science from Pennsylvania State University advised by Prof. James Z. Wang. He has focused on computer vision, distributed systems, security and networking areas. His current work focused on resource management in parallel framework such as MapReduce recently published in IEEE Transactions on Cloud Computing.
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