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Queueing theory based modeling and optimal scheduling in Map-Reduce-like Frameworks

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FORK-JOIN QUEUE MODELING AND OPTIMAL SCHEDULING IN PARALLEL PROGRAMMING FRAMEWORKS
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 thesis 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 equilibriu…

FORK-JOIN QUEUE MODELING AND OPTIMAL SCHEDULING IN PARALLEL PROGRAMMING FRAMEWORKS

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FORK-JOIN QUEUE MODELING AND OPTIMAL SCHEDULING IN PARALLEL PROGRAMMING FRAMEWORKS 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 thesis 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 equilibri…

Farshid Farhat 's Site @ PSU

Farshid Farhat 's Site @ PSU

PublicationsDetecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval, Z Zhou, F Farhat, JZ Wang, IEEE Transactions on Multimedia, 2017. [code][dataset]Shape matching using skeleton context for automated bow echo detection, MM Kamani, F Farhat, S Wistar, JZ Wang, Big Data (Big Data), 2016 IEEE International Conference on, 901-908, 2016.Detecting Vanishing Points in Natural Scenes with Application in Photo Composition Analysis, Z Zhou, F Farhat, JZ Wang, arXiv preprint arXiv:1608.04267, 2016.Optimal Placement of Cores, Caches and Memory Controllers in Network On-Chip, DZ Tootaghaj, F Farhat, arXiv preprint arXiv:1607.04298, 2016.Towards Stochastically Optimizing Data Computing Flows, F Farhat, DZ Tootaghaj, M Arjomand, arXiv preprint arXiv:1607.04334, 2016.Stochastic Modeling and Optimization of Stragglers, F Farhat, D Zad Tootaghaj, Y He, et al. IEEE Transaction on Cloud Computing, 14, 6, 2016.Stoch…

MAX AMINI @ Penn State

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ISA is proud to have the popular and super comedian Max Amini here at Penn State again on March 18th. Get your tickets at Max’s website at “www.maxamini.com” before they are sold out!