Minimizing Cost of Virtual Machines for Deadline-Constrained MapReduce Applications in the Cloud
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions
Journal of Grid Computing
Utility-Driven share scheduling algorithm in hadoop
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Game-based scheduling algorithm to achieve optimize profit in mapreduce environment
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments
The Journal of Supercomputing
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
A MapReduce task scheduling algorithm for deadline constraints
Cluster Computing
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User constraints such as deadlines are important requirements that are not considered by existing cloud-based data processing environments such as Hadoop. In the current implementation, jobs are scheduled in FIFO order by default with options for other priority based schedulers. In this paper, we extend real time cluster scheduling approach to account for the two-phase computation style of MapReduce. We develop criteria for scheduling jobs based on user specified deadline constraints and discuss our implementation and preliminary evaluation of a Deadline Constraint Scheduler for Hadoop which ensures that only jobs whose deadlines can be met are scheduled for execution.