The researcher's predilection towards the concerned infinite resources and the dynamic provisioning on rental premises encourages the scheduling of complex scientific applications in the cloud. The scheduling of workflows in the cloud is constrained to QoS parameters. Many heuristic and meta-heuristic algorithms are widely investigated for the QoS constrained workflow scheduling problem. However, it is still an open area of research, as most of the existing techniques concentrate on minimization of either cost or time and ignores the optimization of multiple QoS constraints simultaneously. To address this problem, in this paper, a Hybrid Spider Monkey Optimization (HSMO) algorithm has been proposed. The proposed algorithm optimizes the makespan and the cost while satisfying the budget and deadline constraints. The proposed algorithm is the hybridization of recently developed SMO and the other popular heuristic BDSD algorithm. BDSD is a budget and deadline constrained algorithm, which guides HSMO in generating a feasible schedule. Moreover, the proposed strategy involves the penalty function to restrict selecting those solutions that fail to satisfy the QoS constraints. Experimental results demonstrate the effectiveness of HSMO over existing ABC, Bi-Criteria PSO, and BDSD algorithms.

研究人员对相关无限资源的偏爱以及在出租场所的动态配置鼓励了在云中调度复杂的科学应用程序。云中的工作流调度受QoS参数约束。针对QoS约束的工作流调度问题，对许多启发式算法和元启发式算法进行了广泛研究。但是，由于大多数现有技术都集中在成本或时间的最小化上，并且同时忽略了多个QoS约束的优化，因此它仍然是一个开放的研究领域。为了解决这个问题，本文提出了一种混合蜘蛛猴优化算法（HSMO）。所提出的算法在满足预算和期限约束的同时优化了制造周期和成本。提出的算法是最近开发的SMO与其他流行的启发式BDSD算法的混合。BDSD是一种预算和期限约束算法，可指导HSMO生成可行的时间表。此外，所提出的策略涉及惩罚函数，以限制选择那些不能满足QoS约束的解决方案。实验结果证明了HSMO相对于现有ABC，Bi-Criteria PSO和BDSD算法的有效性。