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

基于网络测量和用户行为数据，最近的许多工作研究了使用机器学习技术对网络效用和用户体验的建模和预测。虽然它提供了重要的见解，但预测本身往往不是网络的最终目标。理想情况下，网络可以识别体验差的用户，并采取适当的措施主动提高整体性能。为实现这一目标，本项目提倡采用闭环方法，利用学习辅助实用新型明确指导网络中的资源配置，并利用反馈对学习到的实用新型进行验证和改进。这项研究为理解、设计和分析学习模型辅助资源优化算法提供了重要的见解。此外，由于其通用性，该闭环方法可以应用于其他系统，但具有以下特点：1)系统过于复杂，不能仅仅依靠领域知识来构建白盒实用新型；(2)有足够的资料使实用新型能够被学习；3)为了使整体效用最大化，可以对影响效用值的某些控制变量进行优化；4)存在一个反馈回路，以便观察控制的效果。该项目的成果可以应用于不同学科的此类系统。由于网络效用函数的未知和噪声性质，以及在高维、耦合资源约束和非凸优化的背景下，利用该提出的框架是极具挑战性的。为了应对这些挑战，该项目考虑了两种互补的方法：贪婪方法和综合方法。贪心方法在应用多种学习模型方面具有很大的灵活性，在实践中可以更好地适应不同的应用场景，但难以分析。集成方法建立在高斯过程（GP）的基础上，该方法在资源分配决策中集成了构建的模型和模型不确定性。这种方法虽然极具挑战性，但更适合于理论分析。在这两种方法中，都需要基于学习到的模型来优化分配资源。该项目的贡献来自于解决相应的非凸优化问题。最后一步是利用闭环反馈构建更好或最优的实用新型。该集成方法旨在开发用于降维的分层GP强盗算法，理想情况下具有理论性能保证。贪心方法利用一般学习模型的扰动探索方案，力求实用性和通用性。

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.