Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC'22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS, ISCXVPN and ISCXTOR datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called "flowpic'' (i.e., a per-flow 2d histograms of the packets size evolution over time).

Traffic classification, i.e., the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing). This task faces some critical challenges that current deep learning approaches do not address. The design of current approaches do not take into consideration the fact that networking hardware (e.g., routers) often runs with limited computational resources. Further, they do not meet the need for faithful explainability highlighted by regulatory bodies. Finally, these traffic classifiers are evaluated on small datasets which fail to reflect the diversity of applications in real-world settings.

Data center networks are high-bandwidth, low-latency networks with clear-defined topology and busy internal data exchange traffic. TCP is widely used in data centers to transport data, however its default behavior during the flow initialization phase may be unsuitable for data center networks and affect the responsiveness of data transmission, especially when the flow is short. Considering that data center flows happen frequently but only between limited number of data center servers connected by a rather stable physical topology, in this paper we propose to use the RTT records from previous transmissions to help TCP calculate more suitable initialization parameters when a new flow starts, so as to avoid unnecessary lengthy timeouts that has heavy consequences on the overall performance, especially on short flows. Experiments show that our algorithms can indeed eliminate the long timeouts caused by early packet losses, and improve the responsiveness and stability of data transmission in a data center networking environment.

Scheduling algorithms are crucial for most existing switches to improve the throughput. However, the delay of the switching fabric cannot be guaranteed with such scheduling algorithms. This paper aims to design a novel load-balanced wire-speed multicast switching fabric along with the attractive merits of network coding. We adopt a two-phase self-routing switching fabric constructed by Boolean-multicast concentrators (SRBMCs), where the first SRBMC distributes the incoming cells to its outputs uniformly and the second allows the distributed cells to be self-routed and multicast to their destinations concurrently. To further improve the switching performance, linear network coding is smoothly combined with SRBMC to reduce the packet loss rate. Theoretical analysis and numerical simulation demonstrate that the proposed switching fabric cannot only achieve wire-speed multicast switching but also be recursively constructed into an indefinite large-scale one with such merits as no internal buffers, low complexity, and guarantee in switching delay. Finally, we implement the proposed fabric on Field-Programmable Gate Array and verify its performance in multicast switching.

Due to the explosive growth of multimedia content (especially videos) over the Internet, content-centric networking (CCN) is proposed to remit the problems of modern bandwidth-intensive Internet usage patterns. Additionally, the current streaming media coding is designed for video service for IP networks. However, there are few researches who concentrate on efficient streaming media coding for the CCN pattern. It motivates us to find a suitable video coding to improve the performance of CCN. This paper proposes an advanced caching management strategy by combining the Multiple Description Coding (MDC) to the content items, termed as MDC-Ca. The core parts of CCN, e.g., location-independent naming, name-based routing and in-network caching strategy, are all adjusted to the content communication. Moreover, MDC-Ca can deal with the ruleless content chunk distribution in the caching of network nodes. MDC-Ca was studied in a mathematical analysis model and the scheme performs well in the simulation. Further, we perform the emulation in a practical network. The experimental results from both simulations and practical emulations show the superiority of the proposed caching management strategy.

A good switching fabric should be endowed with the properties of no internal buffers, delay guarantee, low component complexity and high-speed multicast, which are difficult for conventional switching fabrics to achieve, fueling the great interest in designing a new switching fabric that can support large-scale extension and high-speed multicast. Motivated by this, we reuse the self-routing Boolean concentrator network and embed a Multicast Packets Copy Separation (MPCS) in front to construct a load-balanced multicast switching fabric. Concretely, MPCS module replicates the multicast packets and forwards them according to the multicast addresses. The first phase of LB-MSNC is responsible for balancing the incoming traffic into uniform cells while the second phase is in charge of self-routing the cells to their final destinations. Differing from the existing fabrics, LB-MSNC is combined with the merits of network coding against the packet loss. Experimental results and analysis have verified that the proposed fabric is able to achieve high-speed multicast switching and suitable for building super large-scale switching fabric in Next Generation Network(NGN) with all the advantages mentioned above.