Dynamic adaptive streaming over HTTP (DASH) has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data-pushing in HTTP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTTP/2 with the aim to improve the quality of experience (QoE) of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transition probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.
The Very Fast Decision Tree (VFDT) algorithm is a classification algorithm for data streams. When processing large amounts of data, VFDT requires less time than traditional decision tree algorithms. However, when training samples become fewer, the label values of VFDT leaf nodes will have more errors, and the classification ability of single VFDT decision tree is limited. The Random Forest algorithm is a combinational classifier with high prediction accuracy and noise-tolerant ability. It is constituted by multiple decision trees and can make up for the shortage of single decision tree. In this paper, in order to improve the classification accuracy on data streams, the Random Forest algorithm is integrated into the process of tree building of the VFDT algorithm, and a new Random Forest Based Very Fast Decision Tree algorithm named RFVFDT is designed. The RFVFDT algorithm adopts the decision tree building criterion of a Random Forest classifier, and improves Random Forest algorithm with sliding window to meet the unboundedness of data streams and avoid process delay and data loss. Experimental results of the classification of KDD CUP data sets show that the classification accuracy of RFVFDT algorithm is higher than that of VFDT. The less the samples are, the more obvious the advantage is. RFVFDT is fast when running in the multi-thread mode.
The development of cloud computing has made container technology a hot research issue in recent years. The container technology provides a basic support for micro service architecture, while container networking plays an important role in application of the container technology. In this paper, we study the technical implementation of the Flannel module, a network plug-in for Docker containers, including its functions, implementation principle, utilization, and performance. The performance of Flannel in different modes is further tested and analyzed in real application scenarios.
Cloud computing faces a series of challenges, such as insufficient bandwidth, unsatisfactory real-time, privacy protection, and energy consumption. To overcome the challenges, edge computing emerges. Edge computing refers to a process where the open platform that converges the core capabilities of networks, computing, storage, and applications provides intelligent services at the network edge near the source of the objects or data to meet the critical requirements for agile connection, real-time services, data optimization, application intelligence, security and privacy protection of industry digitization. Edge computing consists of three elements: edge, computing, and intelligence. Edge computing and the Internet of Things (IoT) mutually create, and edge computing and cloud computing complement each other. In the architecture of edge computing, resources are distributed to the edge nodes, and therefore the storage system is near users while the computation function is near data. In this way, the stress on the backbone network can be lessened. With this architecture, the existing key technologies for computation, networks, and storage will change significantly. ZTE’s edge computing solutions can ensure the service quality of operators and greatly enhance the experience of mobile users.
The challenges of power consumption and memory capacity of computers have driven rapid development on non-volatile memories (NVM). NVMs are generally faster than traditional secondary storage devices, write persistently and many offer byte addressing capability. Despite these appealing features, NVMs are difficult to manage and program, which makes it hard to use them as a drop-in replacement for dynamic random-access memory (DRAM). Instead, a majority of modern systems use NVMs through the IO and the file system abstractions. Hiding NVMs under these interfaces poses challenges on how to exploit the new hardware’s performance potential in the existing system software framework. In this article, we survey the key technical issues arisen in this area and introduce several recently developed systems each of which offers novel solutions around these issues.