Rule Based Distributed Data-Service Management

In this work, a rule-based data management system is proposed for heterogenous cloud data service architectures. For the management of data, many web services and an overarching control mechanism that communicates with these services quickly and accurately to obtain a reliable system are required. Hence, the proposed rule-based system communicates with the web services regularly and processes the designated rules using a multi-layer queuing model for scheduling. The data service architecture consists of three main parts: user management, rule management, and rule processing. These parts contain data services that require high security, the system safety is provided with a two-level protection mechanism namely authentication and authorization. Scheduling system consists of queues where jobs are inserted to be processed. The proposed system also considers the interdependence of sub-jobs. The queue system is created to use a priority queue. A custom Graduate School Administrative Board Decisions Management System (GSBS) is designed and implemented to test and verify the limits of the proposed system. The proposed multi-layer queuing approach is then compared with the commonly used FIFO approach. Test results demonstrate that the proposed Rule Based Distributed Data-Service (RBDDS) is efficient in terms of scalability, response time and throughput metrics.