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Today, the entire world is facing incredible health and economic challenges due to the rapid spread of the life threatening novel Coronavirus Disease - 2019 (COVID-19). In the prevailing situation when a vaccine is many months away, the way forward seems to be a controlled exit from the lockdown - where, infected/exposed people are strictly quarantined and recovered/unexposed people are allowed to carry on with their day to day business activities. However, appropriate physical distancing norms will have to be strictly followed for such relaxations. Therefore, mechanisms are required that will assist people in following the social and physical distancing norms in public places. In this paper, we propose an anonymity preserving blockchain based framework that allows people, through use of their smart phones and other communication devices, to protect themselves from infections as they conduct their daily business activities.

Effective ground water management often requires models that can accurately predict system responses simultaneously and due to urban areas where population is increasing rapidly the water supply system should be more effective manner. In this paper we propose a SCADA base complete water supply system. By simulation it is shown that it is more effective than other system.

The complex distributed environment is a major hindrance in the service delivery process as per the customer requirement. Provisioning resources with minimalist conflict and on-demand pay-per-use service has occupied the center stage of service computing in recent times. The paper tries to address the issue of resource provisioning in a dynamic environment by adopting a biologically inspired approach. Using a linear combination of two different models the paper elucidates strategies for allocation of resources in stable and volatile scenarios. Agent technology plays a key role in controlling the parameters for optimized allocation.

Revenue optimization of large data centers is an open and challenging problem. The intricacy of the problem is due to the presence of too many parameters posing as costs or investment. This paper proposes a model to optimize the revenue in cloud data center and analyzes the model, revenue and different investment or cost commitments of organizations investing in data centers. The model uses the Cobb-Douglas production function to quantify the boundaries and the most significant factors to generate the revenue. The dynamics between revenue and cost is explored by designing an experiment (DoE) which is an interpretation of revenue as function of cost/investment as factors with different levels/fluctuations. Optimal elasticities associated with these factors of the model for maximum revenue are computed and verified . The model response is interpreted in light of the business scenario of data centers.

The research report addresses two fundamental problems of cloud computing. The first part of the report addresses the optimization issues in dynamic resource provisioning. The relevant models experimented with a few bioinspired heuristic algorithms with promising results. The next part of the report has experimented with a few economic models for maximization of revenue in a cloud data center. Quality of Service (QoS) parameters have been investigated and new models in dealing with the complex task of resource management have been proposed.

Data centre is a facility, which houses computer systems and associated components, such as telecommunications and storage systems. Due to the emergence of data centre-based computing services, it has become necessary to examine how the costs associated with data centres evolve over time, mainly in view of efficiency issues. We have presented a quasi form of Cobb-Douglas model, which addresses revenue and profit issues in running large data centres. The stochastic form has been introduced and explored along with the quasi Cobb-Douglas model to understand the behaviour of the model in depth. Harrod neutrality and Solow neutrality are incorporated in the model to identify the technological progress in cloud data centres. This allows us to shed light on the stochastic uncertainty of cloud data centre operations. The insightful results are found to be quite useful in view of production reorganisation in large data centres' around the world.