Yuxiang Hu

Super spread detection has been widely applied in network management, recommender systems, and cyberspace security. It is more complicated than heavy hitter owing to the requirement of duplicate removal. Accurately detecting a super spread in real-time with small memory demands remains a nontrivial yet challenging issue. The previous work either had low accuracy or incurred heavy memory overhead and could not provide a precise cardinality estimation. This paper designed an invertible sketch for super spread detection with small memory demands and high accuracy. It introduces a power-weakening increment strategy that creates an environment encouraging sufficient competition at the early stages of discriminating a super spread and amplifying the comparative dominance to maintain accuracy. Extensive experiments have been performed based on actual Internet traffic traces and recommender system datasets. The trace-driven evaluation demonstrates that our sketch actualizes higher accuracy in super spread detection than state-of-the-art sketches. The super spread cardinality estimation error is 2.6–19.6 times lower than that of the previous algorithms.

To bring 5G systems and networks to life in large-scale commercial applications, academia community has started the research beyond 5G (B5G), in which Network Slicing (NS) is proposed as a new paradigm for building service-tailored B5G networks. In each network slice, to precisely control the service quality and cost, deploying the service-required virtual network functions (VNFs) by utilizing the linkage between the characteristics of this slicing task and the characteristics of different servers in the B5G network is essential. Therefore, aiming at gaining the ability of learning and adapting new tasks quickly and cost-effectively, we view the NFV deployment problem as a meta relational learning process that explores the meta mapping relation between service-tailored slicing tasks and the B5G physical network, and propose a service-tailored VNF deployment framework, abbreviated as StailNet. Instead of training a one-strategy-fits-all deployment model, we focus on “learning” how to train a deployment model, and propose to learn the features of servers and slicing tasks from the perspective of knowledge graph-based representation learning, then locate the initial meta mapping relation by extracting meta information in the task-agnostic meta space and exploring the service-tailored meta mapping relation in the task space for each task, so that we can quickly obtain the solution by a few gradients on the initial meta mapping relation. To highlight the performances of StailNet, we do comprehensive simulations. Simulation results demonstrate that our StailNet outperforms the selected representative algorithms in the literature.

Super spread detection has been widely applied in network management, recommender systems and cyber space security, which is more complicated than heavy hitter, owing to the requirement of duplicate removal. Accurately detecting super spread in real-time with small memory remains a nontrivial yet challenging issue. The previous work either had low accuracy or incurred heavy memory overhead and could not provide precise cardinality estimation. This paper designs an invertible sketch for super spread detection with small memory and high accuracy. It introduces a power-weakening increment strategy that create an environment encouraging sufficient competition at the early stages of discriminating super spread and amplifying the comparative dominance to maintain accuracy. Extensive experiments have been performed based on actual Internet traffic traces and recommender system dataset. The trace-driven evaluation demonstrates that our sketch actualizes higher accuracy in super spread detection than state-of-the-art sketches. The super spread cardinality estimation error is 2.6-19.6 times lower than that of the previous algorithms.

The traffic scheduling mechanism in Time-Sensitive Networking (TSN) is the key to guaranteeing the deterministic transmission of traffic. However, when time-sensitive traffic and non-time-sensitive traffic are transmitted together, traffic scheduling conflicts are easy to occur in TSN. As a result, the deterministic transmission of time-sensitive traffic will be disrupted, and non-time-sensitive traffic may be preempted for a long time. To optimize the performance of multi-type hybrid traffic scheduling in TSN, we firstly establish a collaborative scheduling framework that incorporates Time Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF) mechanisms. We then design a traffic shaping method in this framework based on Least Laxity First (LLF), which considers traffic characteristics to dynamically arrange the time slot injection sequence for different types of traffic. Finally, the traffic schedulability is evaluated based on the scheduling constraints of different types of traffic. Compared with the existing scheduling strategies, the proposed hybrid traffic scheduling strategy can schedule more non-time-sensitive traffic and achieve better delay performance of rate-constrained traffic in different hybrid traffic scenarios. When the number of flows is 100, the time slot injection ratio is increased by 24.3% compared with the LLF_TAS method.

Aiming at examining the problems of the low cache hit ratio and high-average routing hops in named data networking (NDN), this paper proposes a cache-optimization strategy based on dynamic popularity and replacement value. When the requested content arrives at the routing node, the latest popularity is calculated based on the number of requests in the current cycle and the popularity of the previous cycle. We adjust the node cache threshold according to the occupation of the node cache space and cache the content with a higher popularity than the threshold. When the cache is complete, the cache-optimization strategy considers the last request time, popularity, and transmission cost of cached content to calculate the replacement value of cached content. We move the content with the lowest replacement value out of the cache, and keep the content with a high replacement value. We deploy the proposed cache-optimization strategy by using a programmable language in a real network with programmable devices. The experimental results illustrate that the strategy proposed in this paper can effectively improve the cache hit ratio and reduce the average routing hops for user request responses compared with other traditional NDN caching strategies.

With the development of network technology, various network protocols different from TCP/IP have emerged. The heterogeneous integrated network has been proposed to realize the interconnection between heterogeneous networks running different protocols. However, current protocol conversion mechanisms often can only handle a few pre-defined protocols and do not support the flexible expansion of new protocols, which cannot meet the needs of the efficient convergence of different heterogeneous networks. Addirionally, due to the lack of security mechanisms, data in the core network is confronted with the risk of stealing and tampering. Our aim is to provide a protocol-extensible protocol conversion and secure transmission integration mechanism, MultiSec, for heterogeneous converged networks. First, based on the programmable data plane, the parser is reconfigured to realize multi-protocol parsing. Furthermore, the encryption mechanism implemented in the P4 extern is proposed and unified to the data plane together with the protocol conversion mechanism. Finally, the MultiSec prototype is implemented on a programmable software switch and accelerated by a dedicated encryption card. Experiments show that MultiSec successfully realizes multi-protocol conversion and data encryption, and the system performance is significantly improved with the help of an encryption card.

Burdened with the specialized bearing requirements brought by the deep fusion of the Internet and the economic society, the current Internet is faced with a series of great challenges including intelligence, diversification, personalization, robustness and efficiency because of some defects such as rigid structure, simple bearing structure based on IP, and disability in suppressing unknown threats. This paper proposes to improve the function, performance, efficiency and security of the Internet from the perspective of network architecture, introduces the “structure definability” to all layers of the network, and provides a network architecture with a multi-modal presentation of network functions in all layers called Polymorphic Smart Network (PINet). PINet supports full-dimensional definition and multi-modal presentation of addressing and routing, switching mode, interconnection mode, network element, transmission protocol, service properties and so on. Experimental results have verified that PINet supports the incremental evolution of the Internet and fundamentally meets the business requirement of network intelligence, diversification, personalization, high robustness and high efficiency.

Aiming at the problem of failure recovery in current networks, a fast failure recovery method based on equivalent cooperative routing is proposed. Firstly, the transmission path between the source and destination nodes is divided into several non-overlapping path segments. Next, backup paths are deployed for each link in the path segment through segmented routing technology, which ensures fast routing recovery after failure. Additionally, in order to avoid damaging the QoS of the data stream through the failure recovery process, the transmission is guaranteed by the intersegment QoS complement. The experimental results show that the proposed method has a low failure recovery delay under a relatively small flow table cost.