Mobile live-streaming applications with stringent latency and bandwidth requirements have gained tremendous attention in recent years. Encountered with bandwidth insufficiency and congestion instability of the wireless uplinks, multi-access networking provides opportunities to achieve fast and robust connectivity. However, the state-of-the-art multi-path transmission solutions are lack of adaptivity to the heterogeneous and dynamic nature of wireless networks. Meanwhile, the indispensable video coding and transformation bring about extra latency and make the video delivery vulnerable to network throughput fluctuation. This paper presents AggDeliv, a framework that provides efficient and robust multi-path transmission for mobile live video delivery. The key idea is to relate multi-path packet scheduling to congestion control optimization over diverse wireless links and adapt it to the mobile video characteristics. This is achieved by probabilistic packet allocation based on links’ congestion windows, wireless-oriented delay and loss aware congestion control, as well as lightweight video frame coding and network-adaptive frame-packet transformation. Real-world evaluations demonstrate that our framework significantly outperforms the state-of-the-art solutions on aggregate goodput and streaming video bitrate.

Traditional tools like NetFlow face great challenges as both the speed and the complexity of the network traffic increase. To keep the pace up, we propose HashFlow for more efficient and accurate collection of flow records. HashFlow keeps large flows in its main flow table and uses an ancillary table to summarize the other flows when the main table is full. With ourflow collision resolutionandflow record promotionschemes, a flow in the ancillary table is promoted back to the main flow table with a guaranteed probability when it becomes large enough. These operations can be performed highly efficiently, so HashFlow can keep up with ultra-high traffic speed. We implement HashFlow in a Tofino switch, and using traces from different operational networks, we compare its performance against some state-of-the-art flow measurement algorithms. Our experiments show that, for various types of traffic analysis applications, HashFlow consistently demonstrates clearly better performance than its competitors. For example, the performance of HashFlow in flow size estimation, flow size distribution estimation and heavy hitter detection is up to 21, 60 and 35 percent better than those of the best competitors respectively, and these merits of HashFlow come with almost no degradation of throughput.

Flow-based network measurement enables operators to perform a wide range of network management tasks in a scalable manner. Recently, various algorithms have been proposed for flow record collection at very high speed. However, they all focus on processing traffic in a short time window, but overlook the fact that flow measurements are typically needed continuously for unlimited time. To this end, we propose a new algorithm named SuperFlow to support continuous and accurate flow record collection at very high speed by monitoring the flow activeness and exporting the inactive records from the data plane automatically. Our data structures and the corresponding algorithms are carefully designed and analyzed, so the above goal is achieved with limited memory and bandwidth consumption. We implement SuperFlow on both x86 CPU and state-of-the-art PISA target. Comprehensive experiments show that SuperFlow consistently outperforms its competitors significantly. Especially, compared with the best competitor, it records around 136.7% more flows, reduces the error in flow size estimation by 51.5%, and reduces the memory or bandwidth consumption by up to 71.0%, while bringing only negligible throughput degradation.

Collecting flow records is a common practice of network operators and researchers for monitoring, diagnosing and understanding a network. Traditional tools like NetFlow face great challenges when both the speed and the complexity of the network traffic increase. To keep pace up, we propose HashFlow, a tool for more efficient and accurate collection and analysis of flow records. The central idea of HashFlow is to maintain accurate records for elephant flows, but summarized records for mice flows, by applying a novel collision resolution and record promotion strategy to hash tables. We have implemented HashFlow as well as several latest flow measurement algorithms in a P4 software switch, and use traces from different operational networks to evaluate the algorithms. In these experiments, for various types of traffic analysis applications, HashFlow consistently demonstrates a clearly better performance against its state-of-the-art competitors. For example, using a small memory of 1 MB, HashFlow can accurately record around 55K flows, which is often 12.5% higher than the others. For estimating the sizes of 50K flows, HashFlow achieves a relative error of around 11.6%, while the estimation error of the best competitor is 42.9% higher. It detects 96.1% of the heavy hitters out of 250K flows with a size estimation error of 5.6%, which is 11.3% and 73.7% better than the best competitor respectively. At last, we show these merits of HashFlow come with almost no degradation of throughput.

In typical data center networks, an overwhelming majority of the flows are smaller than 200 KB in size, while most transmitted bytes are from a small fraction of large flows. The small flows are usually from the applications interacting with end users, thus they require small completion times. Meanwhile, the data center owners hope to keep the high throughput of the network to make full use of their investments on the network devices. To reduce the completion times of small flows while maintaining the high throughput of the network, we propose a novel transport algorithm, SCT (Transport with Slight Congestion), in this paper. SCT gives small flows higher priority by increasing their congestion windows at a higher rate. Moreover, SCT keeps the network to be in high utilization, thus the throughput of network is guaranteed. Extensive simulations show that SCT can reduce the average completion time of small flows by up to 48% at the expense of degrading the throughput of network by 5% only, compared with DCTCP.

The previous surveys show that more than 90% of the flows in typical data center networks are smaller than 100KB in size, while most bytes transmitted are from a few large flows. The small flows are usually sensitive to their completion times while the large flows require a high throughput. The previous works usually either achieve low completion times for small flows or high throughput for large flows, but not both. In this paper, we propose PTT (Priority-based and Throughput-guaranteed Transport) to minimize the average completion time of small flows while guaranteeing the high throughput of large flows. We conduct comprehensive simulations to evaluate the performance of PTT. The simulation results show that PTT reduces the average completion time of small flows by up to 27.52% over DCTCP and 23.82% over L2DCT while the throughput of large flows is comparable to that in DCTCP, which is 170.46% better than that in L2DCT.

Moving target defense is a revolutionary technology to change the pattern of attack and defense, and end-point information mutation is one of the hotspots belonging to this field. In order to counterpoise the defense benefit of end-point information mutation and service quality of network system, the self-adaptive end-point mutation technique based on adversary strategy awareness is proposed. Directed at the blindness problem of mutation mechanism in the course of defense, adversary strategy awareness based on Sibson entropy algorithm is proposed for guiding the choice of mutation mode by discriminating the scanning attack strategy. Aimed at the low availability problem caused by limited network resource and high mutation overhead, satisfiability modulo theories are used to formally describe the constraints of mutation. Finally, theoretical and experimental analysis shows the ability to resist scanning attack and mutation overhead.