{'fraction_non_alphanumeric': 0.025979945305378303, 'fraction_numerical': 0.004102096627164996, 'mean_word_length': 5.399416909620991, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Architectural Vision of Cloud Computing in the Indian Government

The GI (Govt. of India) cloud started in 2014 is built on the state of art technologies and rich architecture with the nationwide network infrastructure and Data Centres located across the country on National and State data centres. This paper investigates, study and analyze the cloud architecture of Govt. of India and suggests modifications that need to be adapted for sustainable development as per the global changing scenario and fulfill the future needs with improved service delivery, increased throughput, and increased efficiency to provide secured cloud services and to minimize the gap between the cloud service providers and end-users. The cloud services are designed for centralized storage and processing. The cloud data centers are generally located thousands of miles away from the end-users where the data is actually generated. The physical distance between the cloud infrastructure and the data source at edge level end-users produces latency for the real-time processing of the huge amount of data generated at the source level. In recent years the automation scenario is changing globally with various emerging technologies such as the Internet of Things (IoT), Wireless Fidelity 6 (Wi-Fi 6), Fifth Generation Mobile Network connectivity (5G), Artificial Intelligence (AI), and Machine Learning, etc. Emerging technologies like IoT, Wi-Fi 6, 5G gives large scope for boundary level computing and generates a very huge amount of data at the data source level produced by the end-users. These technologies require agile real-time processing and analysis of the data at the source level. Edge computing and Fog computing are the distributed architectures that work together, for reduced latency and speedy real-time processing where the data is actually generated by the end-user. According to the new implementation demands, various emerging cloud technologies such as Mobile Cloud Apps, Containers, Serverless, Microservices, Development and Information Technology Operations (DevOps), BlockChain, Fog computing, Edge Computing, and Software-Defined Infrastructure (SDI), etc are proposed for implementation