Elastic Content Delivery Networks (Elastic CDNs) have been introduced to support explosive Internet traffic growth by providing small Content Providers (CPs) with just-in-time services. Due to the diverse requirements of small CPs, they need customized adaptive caching modules to help them adjust the cached contents to maximize their long-term utility. The traditional adaptive caching module is usually a built-in service in a cloud CDN. They adaptively change cache contents using size-scaling-only methods or strategy-adaptation-only methods. A natural question is: can we jointly optimize size and strategy to achieve tradeoff and better performance for small CPs when renting services from elastic CDNs? The problem is challenging because the two decision variables could involve both discrete and categorical variables, where discrete variables have an intrinsic order while categorical variables do not. In this paper, we propose a distribution-guided reinforcement learning framework JEANA to learn the joint cache size scaling and strategy adaptation policy. We design a distribution-guided regularizer to keep the intrinsic order of discrete variables. More importantly, we prove that our algorithm has a theoretical guarantee of performance improvement. Trace-driven experimental results demonstrate our method can improve the hit ratio while reducing the rental cost.

Clustering on multi-view data has attracted much more attention in the past decades. Most previous studies assume that each instance appears in all views, or there is at least one view containing all instances. However, real world data often suffers from missing some instances in each view, leading to the research problem of partial multi-view clustering. To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering. APMC is conceptually simple and easy to implement in practice, besides it has clear intuitions and non-trivial empirical guarantees. Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result. Compared with existing partial multi-view clustering methods, APMC has three notable advantages: 1) it can capture more non-linear relations among instances with the help of kernel-based similarities; 2) it has a much lower time complexity in virtue of a noniterative scheme; 3) it can inherently handle data with negative entries as well as be extended to more than two views. Finally, we extensively evaluate the proposed method on five benchmark datasets. Experimental results demonstrate the superiority of APMC over state-of-the-art approaches.

Learning related tasks in various domains and transferring exploited knowledge to new situations is a significant challenge in Reinforcement Learning (RL). However, most RL algorithms are data inefficient and fail to generalize in complex environments, limiting their adaptability and applicability in multi-task scenarios. In this paper, we propose SelfSupervised Mixture-of-Experts (SUM), an effective algorithm driven by predictive uncertainty estimation for multitask RL. SUM utilizes a multi-head agent with shared parameters as experts to learn a series of related tasks simultaneously by Deep Deterministic Policy Gradient (DDPG). Each expert is extended by predictive uncertainty estimation on known and unknown states to enhance the Q-value evaluation capacity against overfitting and the overall generalization ability. These enable the agent to capture and diffuse the common knowledge across different tasks improving sample efficiency in each task and the effectiveness of expert scheduling across multiple tasks. Instead of task-specific design as common MoEs, a self-supervised gating network is adopted to determine a potential expert to handle each interaction from unseen environments and calibrated completely by the uncertainty feedback from the experts without explicit supervision. To alleviate the imbalanced expert utilization as the crux of MoE, optimization is accomplished via decayedmasked experience replay, which encourages both diversification and specialization of experts during different periods. We demonstrate that our approach learns faster and achieves better performance by efficient transfer and robust generalization, outperforming several related methods on extended OpenAI Gym’s MuJoCo multi-task environments.

Today's mobile users achieve unsatisfactory quality of experience mainly due to the large network distance to the centralized infrastructure. To improve users' experiences, caching at the wireless access points (APs) has been proposed for bringing the contents closer to users. However, the wireless content placement is challenging as the placement is affected by many realistic constraints, such as a large number of APs, interaction among neighboring APs, various local content popularities. In this paper, we study the wireless caching problem, i.e., which contents should be stored by which APs. First, we fulfil these constraints to formulate our problem and introduce an objective function that maximizes the total cache hit rate of all APs. Next, we prove the NP-hardness of the problem and propose a local distributed caching algorithm to address it. Furthermore, we provide a game theoretic perspective on the problem and prove that the proposed algorithm can converge to the Nash Equilibrium in polynomial time. Finally, we perform simulations on a real-world dataset to demonstrate the effectiveness of our algorithm.

Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks-from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users' homes)-to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU.