, a scheduling model is considered for multiple MapReduce jobs. The goal in is to design an automatic job scheduler that minimizes the makespan of such a set of MapReduce jobs. In this work, we find that there is a key assumption in which leads to the violation of the conditions for classical Johnson’s algorithm and a suboptimal job scheduling for minimizing total makespan. By considering a better strategy and implementation, we can still meet the conditions of classical Johnson’s algorithm. Then we can still use Johnson’s algorithm for an optimal solution. As for BalancedPools algorithm proposed in paper , under our proposed new strategy, it is possible to solve it exactly in linear time, but not NP-hard as suggested in , the proof is provided. With the new strategy, results obtained in need reevaluating." /> , a scheduling model is considered for multiple MapReduce jobs. The goal in is to design an automatic job scheduler that minimizes the makespan of such a set of MapReduce jobs. In this work, we find that there is a key assumption in which leads to the violation of the conditions for classical Johnson’s algorithm and a suboptimal job scheduling for minimizing total makespan. By considering a better strategy and implementation, we can still meet the conditions of classical Johnson’s algorithm. Then we can still use Johnson’s algorithm for an optimal solution. As for BalancedPools algorithm proposed in paper , under our proposed new strategy, it is possible to solve it exactly in linear time, but not NP-hard as suggested in , the proof is provided. With the new strategy, results obtained in need reevaluating." /> , a scheduling model is considered for multiple MapReduce jobs. The goal in is to design an automatic job scheduler that minimizes the makespan of such a set of MapReduce jobs. In this work, we find that there is a key assumption in which leads to the violation of the conditions for classical Johnson’s algorithm and a suboptimal job scheduling for minimizing total makespan. By considering a better strategy and implementation, we can still meet the conditions of classical Johnson’s algorithm. Then we can still use Johnson’s algorithm for an optimal solution. As for BalancedPools algorithm proposed in paper , under our proposed new strategy, it is possible to solve it exactly in linear time, but not NP-hard as suggested in , the proof is provided. With the new strategy, results obtained in need reevaluating." /> A Note on “Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan”
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Issue No.04 - July-Aug. (2014 vol.11)
pp: 390-391
Xinyang Wang , Department of Computer Science , University of Electronic Science and Technology of China, Sichuan, China
ABSTRACT
In paper , a scheduling model is considered for multiple MapReduce jobs. The goal in is to design an automatic job scheduler that minimizes the makespan of such a set of MapReduce jobs. In this work, we find that there is a key assumption in which leads to the violation of the conditions for classical Johnson’s algorithm and a suboptimal job scheduling for minimizing total makespan. By considering a better strategy and implementation, we can still meet the conditions of classical Johnson’s algorithm. Then we can still use Johnson’s algorithm for an optimal solution. As for BalancedPools algorithm proposed in paper , under our proposed new strategy, it is possible to solve it exactly in linear time, but not NP-hard as suggested in , the proof is provided. With the new strategy, results obtained in need reevaluating.
INDEX TERMS
Schedules, Clustering algorithms, Educational institutions, Software algorithms, Processor scheduling, Computational modeling, Algorithm design and analysis,minimized makespan, Hadoop, MapReduce, batch workloads, optimized schedule
CITATION
Xinyang Wang, "A Note on “Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan”", IEEE Transactions on Dependable and Secure Computing, vol.11, no. 4, pp. 390-391, July-Aug. 2014, doi:10.1109/TDSC.2013.55
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