Faculty of Computing

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Has files: YesSubject: search.filters.subject.Management Application

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    Lush: Smart Parking Reservation and Management Application
    (IEEE, 2022-12-26) Koneswara, L; Subasingha, A. S. S. P; Ranaweera, U. R; Wijekoon, W. M. H. A; Swarnakantha, N. H. P. R. S; Chathurika, K.B.A.B.
    The background for this study became the difficulties faced by Sri Lankans in finding a safe and a convenient parking slot. It is obvious that the manual parking systems contain a lot of drawbacks. As a solution a mobile application will be developed for both parking seekers and the parking providers. Parking seekers can register to the app and book a slot. The payment facilities will be provided through the mobile application and customers can chat with the parking providers at any time. System will identify the vehicle category and navigate it to the relevant vehicle type terminal. As well as after parking the vehicle if there is any kind of threat to the vehicles will be detected by the system. If the vehicle owner parks his vehicle at the wrong terminal or if there are any threats happening to the vehicle, the system will send a notification to the vehicle owner and to the security officers. This notification will be sent after detecting the vehicle number plate and getting the owner details from database relevant to the vehicle plate number. Parking providers can digitally and dynamically manipulate the parking area according to their preference through this system. As a conclusion system will facilitate parking seekers by providing user friendly, safe, well-managed and convenient parking facilities on behalf of the parking providers.
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    Artificial Intelligence-Based Centralized Resource Management Application for Distributed Systems
    (IEEE, 2022-12-26) Hettiarachchi, L.S; Jayadeva, S. V; Bandara, R.A.V; Palliyaguruge, D
    Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application’s dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature.