Enhanced coyote optimisation algorithm for task scheduling in computer systems using modified DNN

Cloud computing has evolved into numerous fields and applications in recent years. However, job and resource scheduling can be improved. Task scheduling that maps incoming tasks to resources is needed for high-performance data mapping in heterogeneous computing systems. Cloud computing lets consumers access computational resources online without infrastructure. A service level agreement (SLA) formalises the connection between cloud service customers (CSC) and CSPs. The SP must provide the best features, response time, and resource utilisation to achieve this SLA. Task scheduling is tough in cloud computing since many operations must be done with limited resources. To overcome this difficulty, employ a smart scheduling strategy with strong algorithms that analyse demands and priorities. Thus, this proposal uses the modified deep neural network (MDNN) and improved coyote optimisation algorithm to schedule tasks efficiently. This research aims to reduce energy use and migration costs. The ICOA will set a multi-objective target to efficiently schedule cloud tasks.