EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of using both resource-strenuous edge devices and elastic cloud resources. Efficient resource allocation on such jobs via scheduling algorithms is essential to guarantee their performance, e.g. latency. Deep reinforcement learning (DRL) is increasingly...

Integrated network linear-quadratic model predictive control for disturbance on freeways

We propose an integrated approach for dynamic route guidance and ramp metering using Linear Quadratic Model Predictive Control (LQMPC). The main objective is to maximize the throughput of the network by guiding traffic flow towards to insufficiently utilized infrastructures. Due to the linear property of the LQMPC, it is fast enough for real...