Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds

A cloud is made up of many data centers, with its own set of data and resources. The reasons for employing several cloud sites to operate a workflow are that the data is already dispersed, the required resources surpass the constraints of a single site. This paper presents a hybrid multi-objective optimization algorithm denoted as HSOS-SOA, achieved by combining the Symbiotic Organisms Search and Seagull Optimization Algorithm. The HSOS-SOA uses chaotic maps to generate random numbers and performs a good trade-off between exploration and exploitation, resulting in a higher convergence rate. HSOS-SOA is used to solve scientific workflow scheduling problems in multisite cloud computing by taking into consideration elements such as makespan, cost, and reliability. A solution is chosen from the Pareto front using the knee-point approach in this approach. Extensive analyses are performed out in Microsoft Azure multisite cloud and the results exhibited that the HSOS-SOA can outperform other algorithms in terms of metrics such as IGD, Coverage Ratio, and so on. Experimental results of experiments reveal that the results in makespan improvement in the range of 5.72-28.61%, cost in the range of 5.16-45.16%, and reliability in the range of 3.11-25% over well-known metaheuristic algorithms.