Scientific workflow scheduling using adaptive dingo optimization in multi-cloud environment

A framework for emerging innovations and the capacity to provide reliable cloud services in cloud computing. The availability of “unlimited” computing capabilities to consumers on command is one of the key components of cloud computing. Single cloud holding resources, meanwhile, are typically constrained and could not be capable to handle the unexpected spike in user demands. To allow resource exchange amongst clouds, the multi-cloud architecture is proposed. Offering resources and activities across several clouds is a paradigm that is getting more and more popular today. The majority of existing cloud workflow scheduling projects focus on reducing costs or length of time. The greatest crucial Quality of Service (QoS) parameter, nevertheless, is the dependability of workflow scheduling. As a result, multi-objective scheduling for scientific processing in a multi-cloud architecture is suggested in this research to reduce workflow duration and expense while also satisfying the dependability requirement. To achieve this concept Adaptive Dingo Optimization (ADO) algorithm is designed. The proposed algorithm takes solution encoding, fitness calculation, and update functions. For experimental analysis, a different workflow model is used. The performance of the proposed approach is evaluated in terms of different metrics.