Yonggang Wen

Toward Efficient Compute-Intensive Job Allocation for Green Data Centers: A Deep Reinforcement Learning Approach

2019

Reducing the energy consumption of the servers in a data center via proper job allocation is desirable. Existing advanced job allocation algorithms, based on constrained optimization formulations capturing servers' complex power consumption and thermal dynamics, …

Toward Automated Calibration of Data Center Digital Twins: A Neural Surrogate Approach

2020 · arXiv (Cornell University)

Evolving the computational fluid dynamics (CFD) model to high fidelity digital twin is desirable for industrial data center management. However, existing CFD model calibration approaches to improve the model accuracy require either excessive manual tuning …

Optimizing Data Center Energy Efficiency via Event-Driven Deep Reinforcement Learning

2022 · IEEE Transactions on Services Computing

To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the …

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

2022 · arXiv (Cornell University)

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the …

Joint Input and Output Coordination for Class-Incremental Learning

2024 · arXiv (Cornell University)

Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the …