  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.
