TESCO: Multiple Simulations based AI-augmented Fog computing for QoS Optimization

Iftikhar S., Raj U., Tuli S., GÖLEÇ M., Chowdhury D., Gill S. S., ...Daha Fazla

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022, Haikou, Çin, 15 - 18 Aralık 2022, ss.2092-2099, (Tam Metin Bildiri)

Özet

Fog computing is one of the most widely used paradigms for analyzing and computing the data locally, instead of sending data to remote cloud servers. The main objective of fog computing is to lower the latency of services compared to cloud systems while also reducing the requirement of network bandwidth across computing devices. But as in a fog environment, more data is processed locally, fog nodes usually receive more requests daily, leading to system contention and processing overload. To tackle this, the state-of-the-art relies on machine learning coupled with simulations to forecast processing loads and limit the chances of node contention. However, prior work performs a single simulation to test whether a resource management decision, such as task placement, is optimal or not. This usually ignores the stochastic aspects in the environment or the noise in simulations. To resolve this, we proposed a novel approach called TESCO to perform simulations to test multiple candidate scheduling decisions and decide the optimal one. We demonstrate that TESCO outperforms state-of-the-art scheduler (COSCO) that uses single simulations with low overheads.