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In the popular field of cloud computing, millions of job requests arrive at the data centre for execution. The job of the data centre is to optimally allocate virtual machines (VMs) to these job requests in order to use resources efficiently. In the future smart cities, huge amount of job requests and data will be generated by the Internet of Things (IoT) devices which will influence the designing of optimum resource management of smart cloud environments. The present paper analyses the performance efficiency of the data centre with and without job request consolidation. First, the work load performance of the data centre was analysed without job request consolidation, exhibiting that the job requests to VM assignment was highly imbalanced, and only 5% of VMs were running with a load factor of more than 70%. Then, the technique for order of preference by similarity to ideal solution-based VM selection algorithm was applied, which was able to select the best VM using parameters such as the provisioned or available central processing unit capacity, provisioned or available memory capacity, and state of machine (running, hibernated, or available). The Bitbrains dataset consisting of 1750 VMs was used to analyse the performance of the proposed methodology. The analysis concluded that the proposed methodology was capable of serving all job requests using less than 24% VMs with improved load efficiency. The fewer number of VMs with an improved load factor guarantees energy saving and an increase in the overall running efficiency of the smart data centre environment.

Conventional machine learning (ML) needs centralized training data to be present on a given machine or datacenter. The healthcare, finance, and other institutions where data sharing is prohibited require an approach for training ML models in secured architecture. Recently, techniques such as federated learning (FL), MIT Media Lab's Split Neural networks, blockchain, aim to address privacy and regulation of data. However, there are difference between the design principles of FL and the requirements of Institutions like healthcare, finance, etc., which needs blockchain-orchestrated FL having the following features: clients with their local data can define access policies to their data and define how updated weights are to be encrypted between the workers and the aggregator using blockchain technology and also prepares audit trail logs undertaken within network and it keeps actual list of participants hidden. This is expected to remove barriers in a range of sectors including healthcare, finance, security, logistics, governance, operations, and manufacturing.