Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment

Large-scale applications of the Internet of Things (IoT) necessitate significant computing tasks and storage resources that are progressively installed in the cloud environment. Related to classical computing models, the features of the cloud, such as pay-as-you-go, indefinite expansions, and dynamic acquisition, signify various services to these applications utilizing the IoT structure. A major challenge is to fulfill the quality of service necessities but schedule tasks to resources. The resource allocation scheme is affected by different undefined reasons in real-time platforms. Several works have considered the factors in the design of effective task scheduling techniques. In this context, this research addresses the issue of resource allocation and management in an IoT-enabled CC environment by designing a novel quasi-oppositional Aquila optimizer-based task scheduling (QOAO-TS) technique. The QOAO technique involves the integration of quasi-oppositional-based learning with an Aquila optimizer (AO). The traditional AO is stimulated by Aquila’s behavior while catching the prey, and the QOAO is derived to improve the performance of the AO. The QOAO-TS technique aims to fulfill the makespan by accomplishing the optimum task scheduling process. The proposed QOAO-TS technique considers the relationship among task scheduling and satisfies the client’s needs by minimizing the makespan. A wide range of simulations take place, and the results are investigated in terms of the span, throughput, flow time, lateness, and utilization ratio.