TY - JOUR ID - 90883 TI - An Optimal Utilization of Cloud Resources using Adaptive Back Propagation Neural Network and Multi-Level Priority Queue Scheduling JO - The ISC International Journal of Information Security JA - ISECURE LA - en SN - 2008-2045 AU - Saeed, Anwar AU - Yousif, Muhammad AU - Fatima, Areej AU - Abbas, Sagheer AU - Adnan Khan, Muhammad AU - Anum, Leena AU - Akram, Ali AD - Department of Computer Science, Virtual University, Lahore, Pakistan AD - Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan AD - Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan Y1 - 2019 PY - 2019 VL - 11 IS - 3 SP - 145 EP - 151 KW - Neural Networks KW - Priority Scheduling KW - Multilevel KW - Backpropagation KW - Multi-level Priority Queue Scheduling KW - ABPNN-MLPQS DO - 10.22042/isecure.2019.11.0.19 N2 - With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, on behalf of the comparison of three algorithms like Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR). BR gives a better result with 60 hidden layers Neurons to other algorithms. BR gives 2.05 MSE and 95.8 regressions in Validation, LM gives 2.91 MSE and 94.06 regressions with this and SCG gives 3.92 MSE and 91.85 regressions. UR - https://www.isecure-journal.com/article_90883.html L1 - https://www.isecure-journal.com/article_90883_9774bdaa66b18ed6e55304f52b561b37.pdf ER -