Cost-Based Resource Allocation in Computer Networks using Artificial Intelligence Techniques

A data management process is an elaborate representation of a technical information system which will be with technical information and human resources across all business stages. Cloud automation software utilizes resource allocation methods. These methods are used to monitor the performance of applications and the capacity of data centers. This is done based on the number of requests users make. This study focuses on resource allocation in computer networks aimed at minimizing costs. To achieve this, the study will use artificial intelligence (machine learning and deep learning). The soaring popularity of distributed network (cloud computing) calls for an efficient approach to resource allocation to achieve high performance at low cost. Research proposes intelligent modelling of data supported with algorithms of autoencoder and blue whale feature selection algorithm and integrating this with any intelligent clustering. The model was tested on real-world data, IBM ILOG CPLEX data and SNDlib data. According to the findings, the suggested model outstands other models based on various parameters. In this research, we use deep neural networks to classify the data. The accuracy of proposed method is around 93%.