Browsing by Author "Salvi, S.S."

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    Smart home environment: Artificial intelligence-enabled IoT framework for smart living and smart health
    (IGI Global, 2020) Geetha, V.; Kamath S․, S.; Salvi, S.S.
    Increase in population year by year is making the living status of the urban people difficult as resource-saving and sharing become more challenging. A smart home, which is part of smart city development, provides a better way of handling available resources. Smart home also provides a better way of living with smart devices, which can monitor various activities autonomously. It is also essential to have a smart health system that monitors day to the activity of a person and provides health statistics and indicates health issues at an early stage. The home or devices become smart using artificial Intelligence to analyze the activities. Artificial intelligence provides a way to analyze the data and provide recommendations or solutions based on personalization. In this regard, developing a smart home is essential in the current urban area. This chapter identifies various challenges present in developing a smart home for smart living and smart health and also proposes an AI-based framework for realizing a system with user peronalization and autonomous decision making. © 2021, IGI Global.
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    Virtual Machine Migration in Heterogeneous Clouds - A Practical Approach
    (Institute of Electrical and Electronics Engineers Inc., 2020) Raj, S.; Mangal, N.; Savitha, S.; Salvi, S.S.
    In modern times, Cloud Computing is viewed as more promising technology than any other traditional Information Technology Computing paradigms. It basically serves as an on-demand resource provisioning platform without any active intervention by its user. The resource provisioning strategies require appropriate load distribution management across the cloud network, without which the cloud would face biased workload performance. Virtualization is the backbone of Cloud Computing, which enables the distribution and management of data by initiating the Virtual Machines (VMs). Furthermore, a Cloud Service Provider(CSP) has to monitor, analyze, and manage the workload distribution for servers when VMs are migrated. It presents the need to consider VM migration as an important activity that would unload the cloud server that is overloaded to migrate it to the server that can handle the workload. This paper proposes a technique that initiates the migration of VMs between heterogeneous cloud environments that would lead to a stable and well-balanced cloud network. The process of VM migration is very intensive in terms of resources, and hence intelligent approaches are required. It should effectively reduce the utilization of network bandwidth by minimizing the downtime of the server. However, the migration of VMs between the heterogeneous cloud would be challenging, but the right solution would benefit the cloud network managers on a large scale. Our proposed technique demonstrates heterogeneous VM migration between various cloud platforms built on different architectures. Various parameters have to be technically tuned for the conversion of VM images according to the Cloud Architecture. The performance of the proposed technique is evaluated based on the time taken for migration. © 2020 IEEE.