Adaptive golden eagle optimization based multi-objective scientific workflow scheduling on multi-cloud environment

An exemplary for emerging knowledges and the capacity to provide reliable cloud services, cloud computing. Giving consumers on-demand access to “unlimited” computer resources is one of the key components of cloud computing. Single cloud-holding resources, however, are typically constrained and might not be able to handle the unexpected spike in user demands. In order to support resource sharing amongst clouds, the multi-cloud concept is thus established. These days, offering resources and administrations across numerous clouds is unquestionably amazing. The goal of conventional research on cloud scheduling is to reduce costs or increase speed. However, the major indicator of QoS and a vital problem is the dependability of work process scheduling. As a result, multi-objective scheduling for a logical work process in a multi-cloud environment is suggested in this research with the goal of controlling the work process while also balancing cost and timeliness while satisfying the criterion of reliability. The adaptive golden eagle optimisation (AGEO) algorithm is created to realise this idea. The solution encoding, fitness analysis, and updating functions are used in the proposed algorithm’s validation. Different workflow models are employed for the experimental study, and performance is assessed using various indicators. The projected approach attained 1920 utilization. Similarly, the PSO and GA achieved 1901 and 1900 utilization.