Distributed computing applications provide concurrent processing and services executed from different systems through a common cloud platform. However, without modifications or adaptable security measures, such concurrency presents a great challenge to the proper administration of security. This paper introduces a hybrid secure equivalent computing model to address this security issue. The proposed security model was designed using a genetic algorithm for equivalent measure distribution over the processing systems. Via this model, variations in security management owing to differences in the processing times of various services can be mitigated using the probabilistic annealing method. This method helps to preserve the stability of the security method without decreasing its robustness. For robust processing, the model exploits parallel security as a service feature from the cloud with a non-tokenized key sharing method. The key sharing and revocation processes are determined using the probabilistic outcomes of the annealing method. The genetic process verifies the distribution of security measures in the key sequence of any possible processing combination without compromise. The performance of the proposed model was verified using the metrics of process failure, computational complexity, time delay, false rate, and computing level.

分布式计算应用程序通过公共云平台提供从不同系统执行的并发处理和服务。但是，如果不进行任何修改或采用适应性强的安全措施，这种并发将对安全性的正确管理提出巨大挑战。本文介绍了一种混合安全等效计算模型来解决此安全问题。所提出的安全模型是使用遗传算法设计的，用于在处理系统上进行等效的度量分配。通过该模型，可以使用概率退火方法来减轻由于各种服务的处理时间不同而导致的安全管理上的变化。此方法有助于保持安全性方法的稳定性，而不会降低其鲁棒性。为了进行稳健的处理，该模型利用非令牌化的密钥共享方法，利用云中的并行安全即服务功能。密钥共享和撤销过程是使用退火方法的概率结果确定的。遗传过程可以验证安全措施在任何可能的加工组合的关键顺序中的分布情况，而不会造成影响。使用过程故障，计算复杂度，时间延迟，错误率和计算水平等指标验证了所提出模型的性能。遗传过程可以验证安全措施在任何可能的处理组合的关键序列中的分布情况，而不会造成影响。使用过程故障，计算复杂度，时间延迟，错误率和计算水平等指标验证了所提出模型的性能。遗传过程可以验证安全措施在任何可能的加工组合的关键顺序中的分布情况，而不会造成影响。使用过程故障，计算复杂度，时间延迟，错误率和计算水平等指标验证了所提出模型的性能。