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龙马统数·见微知著大讲堂第65讲:Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques
来源:  点击次数: 次 发布时间:2024-05-07   编辑:太阳成集团tyc234cc

学术报告:Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques

报告时间:5月21日(星期二)下午14:00-15:00

报告地点:学院南路校区,主教213

报告人:赵以强,加拿大卡尔顿大学,教授

报告摘要:In this talk we introduce online algorithms for network resource allocation. The network considered is composed of many servers connected by communication links. The system operates in discrete time; at each time slot, the network administrator reserves resources at servers for future job requests with a cost. Then, after receptions of the resource demand, the jobs may be transferred between the servers to best accommodate the demands with an additional transport cost. Finally, if a job request cannot be satisfied, then there is a violation cost for the blocked job. We propose a randomized online algorithm based on the exponentially weighted method. We prove that our algorithm enjoys a sub-linear in time regret, which indicates that the algorithm is adapting and learning from its experiences, and becomes more efficient in its decision-making as more data become available. Moreover, we tested the performance of our algorithm on simulated data and compare it against a reinforcement learning method. The results show that our proposed method outperforms the reinforcement learning method.

报告人简介:赵教授曾获南京信息科技大学学士学位,加拿大萨斯喀彻温省大学数学与统计学系博士学位,于2003年起任职卡尔顿大学教授,现担任数学和统计学学院的副院长。

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