The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

中文翻译：

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受安全性和可靠性约束的多云计算任务计划

多云系统的兴起已受到鼓舞。对于安全至关重要的任务，重要的是要确保其安全性和可靠性。为了解决异构多云环境中的信任约束，这项工作提出了一种新颖的调度方法，称为匹配和多轮分配（MMA），以优化受安全性和可靠性约束的所有提交任务的工期和总成本。该方法分为两个阶段进行任务调度。第一阶段是为任务找到最匹配的候选资源，以满足他们在多云环境中的优先需求，包括性能，安全性和可靠性；第二个迭代地执行多轮重新分配，以通过最小化估计完成时间的差异来优化任务执行时间和成本。提出的算法，改进的布谷鸟搜索（MCS），混合混沌粒子搜索（HCPS），改进的人工蜂群（MABC），max-min和min-min算法在CloudSim中实现以创建仿真。仿真和实验结果表明，本文提出的方法实现了更短的制造周期，更低的成本，更高的资源利用率以及更好的时间与经济成本之间的权衡。它更加稳定和高效。