Abstract
In cloud, the most prominent area is workflow scheduling due to its widespread application in different domains. It comes under the NP-complete problem, henceforth researchers have suggested the nature-inspired heuristics and metaheuristic algorithms but still, the results of these heuristics are not optimal. These algorithms are not competitive but complementary to each other, so in that case, hybridization may yield better results. Since then, researchers have started to combine different heuristics for better results. But in this process of hybridization, the two most important factors have not been considered yet, namely global and local optimization. The proposed work has considered the two most popular nature-inspired workflow scheduling Cuckoo Search (CS) algorithm and Flower Pollination Algorithm (FPA) for hybridization. CS works best for global optimization, and FPA provides good results in the case of local optimization. The proposed work leveraged the benefits of both optimizations and proposed a novel hybrid algorithm entitled Cuckoo Search Flower Pollination Algorithm (CSFPA). The proposed hybrid algorithm uses multi-objective functions to minimize cost and time and thus enhance the utilization of resources. The CSFPA algorithm has been compared with both single nature aspired algorithms and hybrid algorithm for performance evaluation.