Our paper in the AI4Sys '24 conference at HPDC 2024 presents a novel technique using Generative AI (GenAI) to automate on-the-fly customizations of AI/ML solutions. The ECO-LLM system dynamically adjusts task placement between edge and cloud computing, resulting in minimal performance differences while significantly reducing manual effort and time in solving systems problems.
Our paper presents CLAP, a dynamic solution for optimizing microservice placement across edge and cloud computing in real-time applications. It addresses workload-induced latency issues and cost efficiency by utilizing Reinforcement Learning. Experiments on video analytics demonstrate significant cost reductions of 47% and 58% while maintaining acceptable latency levels.
In The 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), Melbourne, Victoria, Australia Centralized cloud computing with 100+ milliseconds network latencies cannot meet the tens of milliseconds to sub- millisecond response times required for emerging 5G applications like autonomous driving, smart manufacturing, tactile internet, and augmented or virtual reality. We describe … Continue reading ECO: Edge-Cloud Optimization of 5G applications
Giuseppe Coviello 