Cloud computing provides a new way for industries to meet the emerging business need for agility. Many public clouds are available for developers to build web applications on cloud. The process of entering into the cloud is generally in the form of a queue, so that each user need to wait until the current user is being served. In the system, each Cloud Computing User (CCU) requests Cloud Computing Service Provider (CCSP) for use of resources. If CCU finds the server busy, then the user has to wait till the current user completes the job. This may result in increase of queue length as well as waiting time, which may lead to request drop. To handle this problem, CCSP needs to find ways to reduce waiting time. We propose a finite multiserver queueing model with queue dependent heterogeneous servers where the web applications are modeled as queues and the virtual machines are modeled as service providers. CCSP's can use multiple servers and the number of busy servers changes depending on the queue length for reducing queue length and waiting time. This helps us to dynamically create and remove virtual machines in order to scaling up and down. We develop a recursive method to obtain the system steady-state probabilities. Various performance measures of the proposed scheme have been described and evaluated. Computational experiences in the form of graphs are presented.

Most recent research on healthcare systems has focused on integrating the Internet of Things (IoT), Blockchain technology, and cloud computing to enhance the performance of IoT devices with limited resource availability, create smart healthcare platforms, and offer patients the best possible healthcare service. Modern healthcare systems use large-scale sensor devices to address many challenges brought on by the conventional delivery of healthcare services. Most studies have lately identified data collection, massive data processing, geolocating, access management, device prioritization, and storing as primary issues in most IoT healthcare systems. Decentralization, privacy, security, scalability, trust, anonymity, and building geospatial-based intelligent healthcare systems for patient care are significant difficulties that most healthcare systems today must overcome. Blockchain technology in healthcare platforms is noteworthy and innovative since it opens platforms for data privacy, anonymity, and validity through the consensus process. In this work, we proposed a novel decentralized Blockchain-enabled geospatial service architecture for smart healthcare systems called BCGeo . The proposed framework offers an online geospatial healthcare service for residents of Bhubaneswar, a city in India, who are newcomers to the city and are less familiar with its local healthcare organizations. An analytical queueing method prioritizes serving Critical patients more than other patients. In contrast to previously proposed frameworks, the proposed framework includes immutability, scalability, geospatial mapping, patient prioritizing, and decentralized privacy protection policies for addressing the technical challenges in most of the current healthcare systems. Additionally, it explains the performance analysis of BCGeo . It includes graphs showing the various possible outcomes of arithmetic operations, performance measurement, and experimental results on the proposed architecture.