With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence meta-heuristic that has been successfully applied over a wide-range of real-life optimization problems, including task scheduling problems. Besides its strong exploration ability, the CS algorithm suffers from insufficient local search, lack of solution diversity towards the end, and slow convergence problem. These drawbacks produce inefficient cloud task schedules resulting in sub-optimal performance. In this manuscript, an improved CS-based scheduling algorithm called CSDEO is introduced, which combines the features of the Opposition-based learning (OBL) method, Cuckoo search, and Differential evolution (DE) algorithms to optimize workload makespan and energy consumption of the cloud resources. Our CSDEO algorithm firstly uses the OBL method to produce an optimal initial population by providing solutions across the entire solution space. Then, the CSDEO uses an effective way of switching between the CS exploration phase and the DE exploitation phase, depending on each solution's fitness. Experiments are conducted on the CloudSim simulator by using the CEA-Curie and HPC2N supercomputing workloads. The observations show that in the case of CEA-Curie workloads, the proposed CSDEO algorithm achieves makespan improvement in the range of 6.29–29.76% and energy consumption improvement in the range of 3.76–201.98% over well-known scheduling algorithms. In the case of HPC2N workloads, the improvement ranges of the CSDEO approach for the makespan and energy consumption metrics are 9.86–281.69% and 6.12–233.3%, respectively compared to the tested scheduling algorithms.

随着云计算系统使用的迅速增加，需要一种有效的任务调度策略来处理对资源的任务分配，以获得最大的性能。云任务调度（CTS）是已建立的NP-Hard优化问题，可以通过元启发式算法有效解决。布谷鸟搜索（CS）算法是一种功能强大的群智能元启发式算法，已成功应用于各种现实生活中的优化问题，包括任务调度问题。除了其强大的探索能力外，CS算法还遭受本地搜索不足，解决方案多样性不足以及收敛速度慢的问题。这些缺点会导致低效率的云任务计划，从而导致性能欠佳。在这份手稿中 引入了一种改进的基于CS的调度算法CSDEO，该算法结合了基于对立的学习（OBL）方法，布谷鸟搜索和差异进化（DE）算法的功能，以优化工作负载有效期和云资源的能耗。我们的CSDEO算法首先使用OBL方法，通过在整个解决方案空间内提供解决方案来产生最佳的初始种群。然后，CSDEO会根据每种解决方案的适用性，使用有效的方式在CS探索阶段和DE开发阶段之间进行切换。通过使用CEA-Curie和HPC2N超级计算工作负载在CloudSim模拟器上进行了实验。观察结果表明，在CEA-Curie工作负载的情况下，提出的CSDEO算法在6.29–29的范围内实现了有效期的提高。与著名的调度算法相比，能耗降低了76％，能耗降低了3.76–201.98％。在HPC2N工作负载的情况下，与测试的调度算法相比，CSDEO方法的有效期和能耗指标的改进范围分别为9.86–281.69％和6.12–233.3％。