Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Node (networking). The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..

When HTTPS Meets CDN: A Case of Authentication in Delegated Service

Abstract: Content Delivery Network (CDN) and Hypertext Transfer Protocol Secure (HTTPS) are two popular but independent web technologies, each of which has been well studied individually and independently. This paper provides a systematic study on how these two work together. We examined 20 popular CDN providers and 10,721 of their customer web sites using HTTPS. Our study reveals various problems with the current HTTPS practice adopted by CDN providers, such as widespread use of invalid certificates, private key sharing, neglected revocation of stale certificates, and insecure back-end communication. While some of those problems are operational issues only, others are rooted in the fundamental semantic conflict between the end-to-end nature of HTTPS and the man-in-the-middle nature of CDN involving multiple parties in a delegated service. To address the delegation problem when HTTPS meets CDN, we proposed and implemented a lightweight solution based on DANE (DNS-based Authentication of Named Entities), an emerging IETF protocol complementing the current Web PKI model. Our implementation demonstrates that it is feasible for HTTPS to work with CDN securely and efficiently. This paper intends to provide a context for future discussion within security and CDN community on more preferable solutions.

Cache Placement in Fog-RANs: From Centralized to Distributed Algorithms

Abstract: To deal with the rapid growth of high-speed and/or ultra-low latency data traffic for massive mobile users, fog radio access networks (Fog-RANs) have emerged as a promising architecture for next-generation wireless networks. In Fog-RANs, the edge nodes and user terminals possess storage, computation and communication functionalities to various degrees, which provide high flexibility for network operation, i.e., from fully centralized to fully distributed operation. In this paper, we study the cache placement problem in Fog-RANs, by taking into account flexible physical-layer transmission schemes and diverse content preferences of different users. We develop both centralized and distributed transmission aware cache placement strategies to minimize users’ average download delay subject to the storage capacity constraints. In the centralized mode, the cache placement problem is transformed into a matroid constrained submodular maximization problem, and an approximation algorithm is proposed to find a solution within a constant factor to the optimum. In the distributed mode, a belief propagation-based distributed algorithm is proposed to provide a suboptimal solution, with iterative updates at each BS based on locally collected information. Simulation results show that by exploiting caching and cooperation gains, the proposed transmission aware caching algorithms can greatly reduce the users’ average download delay.

Pruning from Scratch

Abstract: Network pruning is an important research field aiming at reducing computational costs of neural networks. Conventional approaches follow a fixed paradigm which first trains a large and redundant network, and then determines which units (e.g., channels) are less important and thus can be removed. In this work, we find that pre-training an over-parameterized model is not necessary for obtaining the target pruned structure. In fact, a fully-trained over-parameterized model will reduce the search space for the pruned structure. We empirically show that more diverse pruned structures can be directly pruned from randomly initialized weights, including potential models with better performance. Therefore, we propose a novel network pruning pipeline which allows pruning from scratch with little training overhead. In the experiments for compressing classification models on CIFAR10 and ImageNet datasets, our approach not only greatly reduces the pre-training burden of traditional pruning methods, but also achieves similar or even higher accuracy under the same computation budgets. Our results facilitate the community to rethink the effectiveness of existing techniques used for network pruning.