Inter-Organizational Cloud Computing and Robust Scalability in Current Scenario and Beyond
Adil Khan, A. R. Junejo, M. Naeem, Mariyam Sattar, Abdul Haseeb Malik · Automatic Control and Computer Sciences · 2022
Abstract Inter-organizational cloud computing model is beneficial for academic institutions. We propose a collaborative inter-institutional cloud learning, where cloud entities can learn the same model cooperatively. The inter-institutional cloud model can reduce network bandwidth costs and ensure privacy. We recommend a trainer to student’s/customer strategy. The key role is to combine a regularization term with the objective function to adjust the gradient of the inter-institutional students/customers under different data. Then, based on the training strategy, a robust federated optimization technique based on joint identification verification is proposed to reduce the number of communication rounds. The main aims of this paper are, first, it can provide a technical platform for cloud services, then institutional resources utilization and opportunistic resources have been evaluated for their overall usage of cloud services and goals. Secondly, our time allocation for cloud computing management workloads is an active area of investigation metrics for the current scenario and control strategy. Experimental results analyze through the different case studies and use the scalability technology compared with the academia and robust scalability techniques.