Nowadays, with the rapid expansion of cloud computing technology in processing Internet of Things (IoT) workloads, the demand for data centers has significantly increased, leading to a surge in CO₂ emissions, power consumption, and global warming. As a result, extensive research and initiatives have been undertaken to tackle this problem. Two specific approaches focus on enhancing workload scheduling, a complex problem known as NP-Hard, and integrating scheduling into scientific workflows. In this investigation, we present a multi-objective Crow Search Algorithm (CSA) for optimizing both makespan and costs in scientific cloud workflows (CSAMOMC). We conduct a comparative analysis between our approach and the well-known HEFT and TC3pop algorithms, which are commonly used for reducing makespan and optimizing costs. Our findings demonstrate that CSAMOMC is capable of achieving an average makespan reduction of 4.42% and a cost reduction of 4.77% when compared to the aforementioned algorithms.

如今，随着云计算技术在处理物联网（IoT）工作负载方面的快速扩展，对数据中心的需求显着增加，导致CO ₂排放量、电力消耗和全球变暖激增。因此，人们进行了广泛的研究和举措来解决这个问题。两种具体方法侧重于增强工作负载调度（称为 NP-Hard 的复杂问题）以及将调度集成到科学工作流程中。在这项研究中，我们提出了一种多目标 Crow 搜索算法 (CSA)，用于优化科学云工作流程 (CSAMOMC) 中的完工时间和成本。我们对我们的方法与著名的 HEFT 和 TC3pop 算法进行了比较分析，这些算法通常用于缩短完工时间和优化成本。我们的研究结果表明，与上述算法相比，CSAMOMC 能够实现平均完工时间缩短 4.42%，成本降低 4.77%。