Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.
翻译:由于现代工作量应用的高度波动性以及低能源消耗和反应时间的敏感用户要求,大型雾化平台的任务的智能配置和管理具有挑战性,因为现代工作量应用的高度波动性和低能源消耗和反应时间的敏感用户要求,集装箱调频平台已经出现,以缓解这一问题,因为此前的艺术要么使用超常性来迅速达成时间安排决定,要么采用人工智能驱动方法,例如强化学习和演化方法来适应动态情景,前者往往无法在高度动态环境中迅速适应,而后者的运行时间则非常缓慢,足以对反应时间产生不利影响。因此,有必要制定既能对在动荡环境中高效工作作出反应又能安排低的调度管理管理管理政策。 为实现这一目标,我们建议采用对投入的梯度进行后推法的渐进优化优化优化战略。 此外,我们利用预测数字双赢模型模型和模拟能力来适应动态的动态环境,利用混合模拟决策时间框架、GOBI* 来优化服务(QOS) 参数的质量。