Thin Client and Service Proxy Architectures for Real-Time Staffing Systems in Distributed Operations
DOI:
https://doi.org/10.15662/dnsbp552Keywords:
Thin Client Architecture, Service Proxy Systems, Real-Time Staffing, Distributed Operations, Cloud ScalabilityAbstract
To provide real-time staffing solutions that align with the emerging challenges in distributed operations, lightweight, scalable and secure system architecture is an emerging need. Client-server models are commonly faced with latency, redundancy and network inefficiency when used in the dynamic deployment of staff across sites. This paper examines thin client and service proxy systems as an effective means to optimise the staffing system in a distributed enterprise. This organization is based on the concept of a thin client which means minimal resources that are necessary to be installed on the end-user device to guarantee a high responsiveness based on the processing centrally. At the same time proxy layers of services provide intermediation among services that improves performance through request handling, balanced loads and fault tolerance in complex contexts. This paper is a novel architectural framework of staffing operations based on the integration of theoreticalnotions of distributed systems, queueing theory, and cloud scalability. The adopted methodology is the simulation-based modeling, the analysis of case studies, and the performance benchmarking in terms of latency, scalability, reliability, and cost-efficiency. Results indicate that thin client and service proxy models can reduce system overhead and improve response times as well as workforce utilization by substantial margins with respect to their traditional counterparts. Moreover, the paper evokes the implications facing enterprises that develop operational agility, especially in regards to its scale, security and interoperability with legacy systems. According to the proposed framework, that integration of the predictive models operated by artificial intelligence will provide the basis of the future real-time staffing solutions.
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