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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments
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COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

机译：COSCO：使用共模和基于梯度的雾化计算环境优化的集装制弦乐集

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摘要

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.
机译：大规模迷雾平台中的智能任务安置和管理在大规模的雾平台上是具有挑战性，因为现代工作负载应用的高度挥发性和低能量消耗和响应时间的敏感用户要求。容器编排平台已经出现了使用HeuRistics使用HeuRistics来缓解这个问题，以便快速达到调度决策或类似于加强学习和进化方法来适应动态方案的驱动方法。前者经常无法快速适应高度动态的环境，而后者的运行时间足够慢以产生负面影响响应时间。因此，需要对挥发性环境中有效工作的反应性的调度策略，并且具有低调度开销。为此，我们提出了一种基于梯度基于梯度的优化策略，相对于输入（GOBI），梯度的反向传播。此外，我们通过开发耦合仿真和容器编程框架（COSCO）来利用预测性数字双模型和模拟能力的准确性。使用此功能，我们创建了一个混合仿真驱动决策方法，GOBI *，优化服务质量（QoS）参数。共模和后传播方法允许这些方法在挥发性环境中快速适应。使用GOBI和GOBI *方法在雾应用上进行实验进行的实验，在能耗，响应时间，服务水平目标和调度时间分别显着改善，分别为15,40,4和82％与最先进的算法相比。

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