NeTS: Small: Learning-Guided Network Resource Allocation: A Closed-Loop Approach

NeTS：小型：学习引导的网络资源分配：闭环方法

• 批准号: 1718901
• 负责人: Xin Liu
• 金额: $ 47.9万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718901&HistoricalAwards=false
• 关键词: NeTS; Small; Learning; Guided; Network

Based on network measurement and user behavior data, much recent work has studied the modeling and prediction of network utility and user experience using machine learning techniques. While it provides important insights, prediction itself is often not the ultimate goal in networks. Ideally, a network could identify users with poor experience and take proper actions to proactively improve the overall performance. To achieve this goal, the project advocates a closed-loop approach that uses learning-aided utility model to explicitly guide resource allocation in networks and uses feedback to (in)validate and improve the learned utility model. This investigation provides important insights in understanding, designing, and analyzing learning-model-aided resource optimization algorithms. Furthermore, because of its generality, this closed-loop approach can be applied in other systems with the following characteristics: 1) the system is too complex to rely on domain knowledge only to build a white-box utility model; 2) there exists sufficient data so that a utility model can be learned; 3) to maximize the overall utility, one can optimize over certain control variables that affect the utility value; and 4) there exists a feedback loop so that the effect of the control can be observed. The outcome of the project can be applied to such systems in different disciplines. Utilizing this proposed framework is highly challenging due to the unknown and noisy nature of the network utility function, and in the context of high dimensionality, coupled resource constraints, and non-convex optimization. To address these challenges, the project considers two complementary approaches: a greedy approach and an integrated approach. The greedy approach has much flexibility in applying diverse learning models, which may fit different application scenarios better in practice, but is difficult to analyze. The integrated approach builds upon Gaussian Process (GP) bandits that integrate both the constructed model and model uncertainty in resource allocation decisions. This approach is more amenable to theoretical analysis, although highly challenging. In both approaches, one needs to optimally allocate resource based on the learned models. The contribution of the project comes from solving the corresponding non-convex optimization problems. The last step is to use the closed-loop feedback to build a better or optimal utility model. The integrated approach aims to develop hierarchical GP bandit algorithms for dimensionality reduction, ideally with theoretical performance guarantees. The greedy approach leverages perturbed-exploration schemes for general learning models and strives for practicality and generality.

基于网络测量和用户行为数据，许多最新工作研究了使用机器学习技术对网络实用程序和用户体验的建模和预测。尽管它提供了重要的见解，但预测本身通常不是网络中的最终目标。理想情况下，网络可以识别经验差的用户并采取适当的措施以主动改善整体性能。为了实现这一目标，该项目提倡一种闭环方法，该方法使用学习辅助实用程序模型明确指导网络中的资源分配，并使用反馈来验证和改善学习的效用模型。这项调查为理解，设计和分析学习模型的资源优化算法提供了重要的见解。此外，由于其普遍性，这种闭环方法可以应用于具有以下特征的其他系统：1）系统太复杂而无法依靠域知识仅构建白色盒子实用程序模型； 2）存在足够的数据，因此可以学习实用性模型； 3）为了最大化整体实用程序，可以优化影响效用值的某些控制变量； 4）存在一个反馈回路，以便可以观察到对照的效果。该项目的结果可以应用于不同学科的此类系统。由于网络公用事业函数的未知和嘈杂性，并且在高维度，耦合资源约束和非convex优化的背景下，使用此提议的框架非常具有挑战性。为了应对这些挑战，该项目考虑了两种互补方法：一种贪婪的方法和一种综合方法。贪婪的方法在应用多种学习模型方面具有很大的灵活性，这在实践中可能更适合不同的应用程序方案，但是很难分析。集成的方法建立在高斯过程（GP）匪徒的基础上，该过程在资源分配决策中同时集成了构建的模型和模型不确定性。这种方法更适合理论分析，尽管极具挑战性。在这两种方法中，都需要根据学习模型来最佳分配资源。该项目的贡献来自解决相应的非凸优化问题。最后一步是使用闭环反馈来构建更好或最佳的实用程序模型。综合方法旨在开发层次的GP BANDIT算法，以降低维度，理想情况下是理论性能保证。贪婪的方法利用了一般学习模型的扰动 - 探索方案，并为实用性和一般性而努力。

项目成果

Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

• 发表时间: 2017-09
• 作者: Huasen Wu;Xueying Guo;Xin Liu

A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning

• 发表时间: 2019-04
• 期刊: ArXiv
• 作者: Shahbaz Rezaei;Xin Liu

IPO: Interior-point Policy Optimization under Constraints

• DOI: 10.1609/aaai.v34i04.5932
• 发表时间: 2019-10
• 期刊: ArXiv
• 作者: Yongshuai Liu;J. Ding;Xin Liu

Deep Learning for Encrypted Traffic Classification: An Overview

• DOI: 10.1109/mcom.2019.1800819
• 发表时间: 2019-05-01
• 期刊: IEEE COMMUNICATIONS MAGAZINE
• 影响因子: 11.2
• 作者: Rezaei, Shahbaz;Liu, Xin
• 通讯作者: Liu, Xin

Cellular Network Configuration via Online Learning and Joint Optimization

通过在线学习和联合优化进行蜂窝网络配置

• 发表时间: 2017
• 期刊: IEEE Big Data Conference
• 作者: Guo, Xueying;Trimponiasy, George;Wang, Xiaoxiao;Chen, Zhitang;Geng, Yanhui;Liu, Xin
• 通讯作者: Liu, Xin