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The pandemic coronavirus diseases 2019 are generating different data sets in different regions of the world The data sets are observed to be available in geographically separated medical entities However, the demand for accessing and reliable delivery of such datasets through a web-based module is increasing gradually In this work, we propose a novel cycle of reliability evaluation model for deployment of Software as a Service-based prototype for the coronavirus disease data processing system We call it as PwCOV The prototype generates clinical remarks through the paradigm of service-oriented computing, cluster-based load balancing web servers, and loosely coupled software principles The applicability of PwCOV for processing isolated disease datasets is discussed against different stress of set of user entities The validity and applicability of the proposed model are evaluated through statistical analysis The reliability of the PwCOV is observed by evaluating the recorded status of the business logic execution, failure count and failure rate The study reveals that the PwCOV is affective for processing disease data set for a collaborative set of tenants A novel methodology is designed for the deployment of software as a service for the COVID-19 data processing system using a load balancing cluster base web server, where the roles of service-oriented computing are segregated among different layers The limitation of such deployment is also discussed for multi-tenant environment © 2020, Materials and Energy Research Center All rights reserved

Evaluating the Reliability of PwCOV: A Loosely Coupled Software as a Service for COVID-19 Data Processing System