Network slicing allows heterogeneous applications can be launched across different domain using virtualized resources. The virtualized resources are created on the physical infrastructure. The orchestrator is essential for coordination of network slice management. The overhead in the slice orchestrator is reduced by distributed approach. The slice template act as a design speci cation template for the creation of network slices. This template can be predicted using federated learning, in which local models are trained with the generated data and global model is trained with local model parameters. Then the global parameters are updated in the local model for further learning. The federated model uses the SDN capability to learn the local model data distribution and hence enhance global SDN federated controller prediction accuracy for network slices. This process can be automated with the help of slice template with the predicted pattern. The parameters of the slice template are directly proportional to the performance of the slice orchestrator and prediction of future slice demands. The edge devices with its local model communicate with the global SDN federated model to satisfy the requirement of dynamic network slicing. The request on-demand services can be provided as virtual network function using network function virtualization. The optimal resource allocation for the requested slice can be done with statistical modeling of observed tra c and autoscaling can be carried out. Experimental studies reveal that the proposed network slicing with federated approach minimal response time with maximal orchestrator scalability.
In the period of BigData, massive amounts of structured and unstructured data are being created every day by a multitude of everpresent sources. BigData is complicated to work with and needs extremely parallel software executing on a huge number of computers. MapReduce is a current programming model that makes simpler writing distributed applications which manipulate BigData. In order to make MapReduce to work, it has to divide the workload between the computers in the network. As a result, the performance of MapReduce vigorously depends on how consistently it distributes this study load. This can be a challenge, particularly in the arrival of data skew. In MapReduce, workload allocation depends on the algorithm that partitions the data. How consistently the partitioner distributes the data depends on how huge and delegate the sample is and on how healthy the samples are examined by the partitioning method. This study recommends an enhanced partitioning algorithm using modified key partitioning that advances load balancing and memory utilization. This is completed via an enhanced sampling algorithm and partitioner. To estimate the proposed algorithm, its performance was compared against a high-tech partitioning mechanism employed by TeraSort. Experimentations demonstrate that the proposed algorithm is quicker, more memory efficient and more accurate than the existing implementation.
Network slicing allows heterogeneous applications can be launched across different domain using virtualized resources. The virtualized resources are created on the physical infrastructure. The orchestrator is essential for coordination of network slice management. The overhead in the slice orchestrator is reduced by distributed approach. The slice template act as a design specification template for the creation of network slices. This template can be predicted using federated learning, in which local models are trained with the generated data and global model is trained with local model parameters. Then the global parameters are updated in the local model for further learning. The federated model uses the SDN capability to learn the local model data distribution and hence enhance global SDN federated controller prediction accuracy for network slices. This process can be automated with the help of slice template with the predicted pattern. The parameters of the slice template are directly proportional to the performance of the slice orchestrator and prediction of future slice demands. The edge devices with its local model communicate with the global SDN federated model to satisfy the requirement of dynamic network slicing. The request on-demand services can be provided as virtual network function using network function virtualization. The optimal resource allocation for the requested slice can be done with statistical modeling of observed traffic and autoscaling can be carried out. Experimental studies reveal that the proposed network slicing with federated approach minimal response time with maximal orchestrator scalability.
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