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For the MMR algorithm, since the base learners which process part of the original datasets work in one single machine sequentially, in the next step, we plan to parallelize this step and distribute the computation to more computing nodes for the sake of increasing the computational efficiency.
It is clear that in both LLR computation and aggregation queries, the runtime of Bus-OLAP decreases when more computing nodes are used.
One possible solution to the computational bottleneck during high volume periods might be to temporarily deploy more computing nodes, either on the local network, or using a computing grid solution.
The problem of this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are added.
In general, we obtained a linear speedup as we added more computing nodes in the cluster.
It is represented by the following equation: speedup={T}_1/{T}_n (3). Figure 6 shows the speedup as more computing nodes are added to the cluster.
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The Scheduler starts the VMs on one or more Compute Nodes, and informs the SLA Manager of the new processes and the load that is expected.
This means that the brain really has 105 more networked computing nodes than the SC5832.
Further, the scalability can be improved by using more number of computing nodes.
Further, the efficiency of proposed approach can be improved by engaging more number of computing nodes in a cluster.
Data and applications will move off the desktop and into more centralized compute nodes, enabling accessibility and collaboration.
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