Many applications, such as AI training and distributed storage, rely on cross-datacenter (DC) networks to provide services. For compatibility with existing RDMA hardware and to improve quality of service, network providers connect to datacenters over dedicated lines. However, the current RDMA congestion control has some problems in cross-DC environment. First, due to the large bandwidth delay product (BDP) of cross-DC flows, the switch frequently triggers PFC, which impairs the transmission of all flows. Meanwhile, due to the lag of congestion signals, congestion control algorithms may cause unfair bandwidth allocation between intra-DC flows and cross-DC flows. In addition, cross-DC traffic will experience severe queuing at the data center interconnect (DCI) switch, increasing queuing delay. To address these challenges, this paper proposes MLCC, a cross-datacenter congestion control algorithm based on the fast control loop. MLCC uses micro congestion control loops to achieve fine-grained network state awareness and accurate rate adaptation, and reduce queue length in the transmission path. Experimental results show that MLCC can quickly converge all flows to fairness, achieve high link utilization, and ensure low queue length on the switch. Large-scale simulations show that MLCC can reduce the average FCT of intra-datacenter and cross-datacenter traffic by up to 46% and 27%, respectively.

Cloud virtual reality (VR) has emerged as a promising technology, offering users a highly immersive and easily accessible experience. However, concurrent pulse VR flows can lead to significant congestion in 5G base stations, making network providers unable to guarantee the delay requirements for all users. Based on a comprehensive analysis of the poor delay per-formance of cloudVR flows within the existing 5G edge network, we propose a novel cross-layer congestion control mechanism that is assisted by access network status and flow characteristics. This mechanism is deployed within the 5G edge network and enables efficient global scheduling of concurrent flows. Experiment results show that our mechanism greatly optimizes the network delay in concurrent scenarios and guarantees the delay requirements of all users while avoiding network overload. Our work underscores the advantage of leveraging 5G edge nodes as a valuable resource to meet the anticipated demands of future services effectively.

VR applications that require extremely low latency and high image quality are widely used in online games and other 5G scenarios, becoming a key research field in recent years. However, the limited bandwidth in 5G edge networks fails to meet the peak rate requirements for multiple VR flows. MPTCP is suitable for 5G edge networks, supporting the simultaneous use of multiple networks on mobile devices. Nevertheless, accurately scheduling VR data blocks to different sub flows to satisfy their low latency requirements is challenging due to their micro-burst characteristic. In this paper, we propose a novel MPTCP scheduler for cloud VR applications in 5G edge networks, called the Cross-Layer Information-based One-Way Delay Predictive Scheduler (CPS). CPS accurately predicts one-way delay by incorporating cross-layer information from both the application and edge wireless sides, and adaptively schedules VR data blocks to the optimal subflow. Experimental results show that CPS outperforms existing strategies, supporting 125% more users for VR applications in the typical scenario. Additionally, CPS maintains completion times for 99% of cloud VR packets below 7 ms. CPS successfully meets the quality of experience needs of more users, providing a promising solution for large-scale deployment of cloud VR services in 5G edge networks.

Matrix multiplication is crucial in scientific computing, but it demands substantial resources. We propose a framework for effectively utilizing heterogeneous GPUs to large matrix multiplication. By splitting matrices into small blocks and using Douglas’s variant of Strassen’s algorithm, we enable concurrent tasks on heterogeneous systems. Our framework improves speed by 89.5% on homogeneous GPU servers and by 108% in multi-server heterogeneous GPU setups.

The millimeter wave technology which provides the throughput of multi-gigabit per second is one of the key technologies for 5G/B5G communications. However, an optimal interaction between the transport layer protocols and the highly fluctuating millimeter wave networks is extremely challenging and lacks in-depth exploration in actual networks. In this paper, we examine and discuss the performance of several TCP congestion control algorithms in the real 60 GHz millimeter wave environment, and inspect the improvement of TCP performance over mmWave hybrid networks by TCP proxies in single-flow and multi-flows scenarios. Our results reveal severe adaptation problems associated with these congestion control algorithms over millimeter wave networks and the effectiveness of TCP proxies for the utilization of millimeter wave hybrid networks.

With the development of communication technologies, vehicular network applications have evolved from basic traffic safety and efficiency applications to information and entertainment applications. The implementation of emerging vehicular applications is based on the efficient dissemination of multimedia data. In view of the dynamic topology changes, severe channel fading and limited spectrum resources of vehicular networks, how to achieve efficient multimedia data dissemination in the harsh network environment is an urgent problem. Based on the hybrid cellular-D2D vehicular network, this paper proposes a cluster-based cooperative multicast scheme. The scheme combines multicast transmission with D2D-assisted relay technology to provide high-quality data dissemination for vehicle users under limited spectrum resources. In this paper, we innovatively present a communication quality index that considers multiple performance factors and formulate the relay selection problem as the anti p-center problem in graph theory. Then we propose a heuristic method to solve the problem. The results show that the proposed scheme can effectively improve the utilization of wireless resources and the success rate of data dissemination.

Motivation: Mass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration. Results: We constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics. Availability and implementation: SCRIPT-MAP database is available at http://www.firmiana.org/responders/. Supplementary information: Supplementary data are available at Bioinformatics online.

Software-defined networking (SDN) simplifies the forwarding devices by introducing a centralized controller. The controller calculates routing rules for the whole network and the forwarding devices cache the routing rules. This working process leads to the new-flow attack. When malicious packets with different headers arrive at the network, they are treated as new flows. These useless flows consume lots of the resources in the controller and the forwarding devices. According to the current solution, suspicious flows are redirected to the security middleware. However, the security middleware can be a bottleneck when lots of flows are redirected to it in a short time. In this paper, we propose SmartSec to prevent the new-flow attack and optimize the security middleware at the same time. SmartSec uses the standard control link message to monitor the new-flow attack, and it achieves a low cost on the control link. Based on the monitoring results, SmartSec redirects the suspicious flows to the security middleware and monitors the workload of the security middleware. An optimization method is designed in SmartSec to reduce the workload of the security middleware. We evaluate our mechanism in both simulator and test bed. The simulation and experiment results verify the performance of SmartSec.