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Matchmaking a new mapreduce scheduling technique

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YARN was introduced in Hadoop 2 to improve the MapReduce implementation, but it is general enough to support other distributed computing paradigms as well. Instead, users write to higher-level APIs provided by distributed computing frameworks, which themselves are built on YARN and hide the resource management details from the user. YARN provides its core services via two types of long-running daemon: a resource manager one per cluster to manage the use of resources across the cluster, and node managers running on all the nodes in the cluster to launch and monitor containers. A container executes an application-specific process with a constrained set of resources memory, CPU, and so on. The resource manager then finds a node manager that can launch the application master in a container steps 2a and 2b. It could simply run a computation in the container it is running in and return the result to the client.
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Matchmaking scheduling algorithm

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PPT – NETWORK SCHEDULING TECHNIQUES PowerPoint presentation | free to download - id: aa-Y2Q0Z

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NETWORK SCHEDULING TECHNIQUES - PowerPoint PPT Presentation

MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner. MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce.
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Effective date : A method for scheduling MapReduce tasks includes receiving a set of task statistics corresponding to task execution within a MapReduce job, estimating a completion time for a set of tasks to be executed to provide an estimated completion time, calculating a soft decision point based on a convergence of a workload distribution corresponding to a set of executed tasks, calculating a hard decision point based on the estimated completion time for the set of tasks to be executed, determining a selected decision point based on the soft decision point and the hard decision point, and scheduling upcoming tasks for execution based on the selected decision point. The method may also include estimating a map task completion time and estimating a shuffle operation completion time.
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