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For example, reducing slot gap by 41% resulted in a 58% reduction in the effective bubble diameter.
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For the reduce phase, typically a 0.95% of the total reduce slot is optimum to reduce task.
For example in Fig. 2, if we used only one reduce task, we would not exploit fully the available parallelism (i.e., the other one reserved reduce slot).
So initially, we reserve one map and one reduce slot for each job (ms j =1 and rs j =1,∀j∈Jobs).
Each time we increase a job's map or reduce slot (or both of them if the VMs' Allocation policy is applied), we also adjust the corresponding tasks (i.e., map/reduce) that will be utilized.
For example, if the cluster consists of nodes that have 8 GB RAM and we want to use one map and one reduce slot in each node then the maximum value of mBufferj will be 4 GB.
In each VM, both map and reduce slots are available.
Second, iiHadoop 1.2.1 uses fixed number of map and reduce slots for each TaskTracker.
msPerVM,rsPerVM: the number of map and reduce slots per VM.
Each Worker node was equipped with two map and two reduce slots.
So in each VM, we have both map and reduce slots.
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