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.

中文翻译：

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COSCO：使用协同仿真和基于梯度优化的雾计算环境容器编排

由于现代工作负载应用程序的高度易变性以及用户对低能耗和响应时间的敏感要求，在大型雾平台中智能任务放置和任务管理具有挑战性。已经出现了容器编排平台，以通过使用启发式快速达到调度决策或人工智能驱动的方法（如强化学习和进化方法来适应动态场景）来缓解现有技术的这一问题。前者通常无法在高度动态的环境中快速适应，而后者的运行时间慢到足以对响应时间产生负面影响。因此，需要一种既能在易变的环境中高效工作又具有低调度开销的调度策略。为了达成这个，我们提出了一种基于梯度的优化策略，使用相对于输入的梯度反向传播（GOBI）。此外，我们通过开发耦合模拟和容器编排框架 (COSCO) 来利用预测性数字孪生模型和模拟功能的准确性。利用这一点，我们创建了一种混合模拟驱动决策方法 GOBI*，以优化服务质量 (QoS) 参数。联合仿真和反向传播方法使这些方法能够在不稳定的环境中快速适应。使用 GOBI 和 GOBI* 方法对雾应用的真实数据进行的实验表明，能耗、响应时间、服务水平目标和调度时间分别提高了 15%、40%、4% 和 82%与最先进的算法相比。