Affiliations: [a] Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| [b] Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India
| [c] Department of Computer Science & Engineering, School of Engineering & Technology, Sharda University, Greater Noida, India
| [d] Department of Computer Engineering, Pillai College of Engineering, Navi Mumbai, India
| [e] Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India
| [f] Department of Information Technology, Manipal University Jaipur, Jaipur, India
Correspondence:
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Corresponding author. Ankit Mundra, Department of Information Technology, Manipal University Jaipur, Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan, India. E-mail: [email protected].
Abstract: This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy.
Keywords: ANN, BB-BC, resource optimization, fault
