SecQSON: Secure Query Scheduling and Ontology-Based Searching in Map-Evaluate-Reduce-Enabled Grid Environment

Task scheduling and resource allocation are the major issues in grid environment. Based on grid user’s requirements such as deadline, cost, and service type, tasks must be scheduled and appropriate resources are allocated for each user task. Previous works in this topic is failed to analyze all criteria for timely scheduling and resource allocation. Further, scalability and storage issues are other drawbacks in grid computing. Grid over Hadoop is a great solution for solving scalability and storage issues. When adding security to the system, we can address the traditional issues of grid computing high response and retrieval time, resource searching time, low scalability, and storage issues. In this paper, we proposed a new framework called as SecQSON which is a secure query scheduling and ontology-based searching in Map-Evaluate-Reduce model. There are three processes which are applied in this paper as authentication, scheduling, and data retrieval phases. In authentication phase, data owners (DO) and data users (DU) are authenticated by trusted authority (TA). For this purpose, Dual Bio-Key-based Random Authentication (DUBK-RA) algorithm is proposed. The security credentials are fingerprint, finger vein, ID, and password for authentication. For bio-key generation, BLAKE-3 hashing algorithm is generated in TA and this key is verified to validate whether the request is authorized or not. Then scheduling phase processes the authorized requests (DU’s) for query scheduling. In this task, Map-Evaluation-Reduce model is proposed that maps the users to optimum grid resources. The evaluation of the resources for user queries is evaluated using Spotted Hyena Optimizer algorithm. For evaluation purpose, various criteria are considered such as trust level, resource score (available bandwidth and queries), and time score (response time and execution time). Final phase is a data retrieval phase in which authorized DO’s records are stored in the grid-connected Hadoop server. In grid server, name node is processed and it constructs the index values for DO’s records by means of Dendrimer Order Statistic (Den-OS) index. Further, ontology is constructed for records stored in data nodes. Grid resources for user queries are dynamically searched, and the optimum search results are retrieved for grid users. Experiments are conducted and the performance is evaluated using several metrics such as response time, search accuracy, retrieval time, authentication time, latency, energy consumption, precision, recall, and f-measure.