Search Results (6)

A sketch is a probabilistic data structure that can accurately estimate massive network traffic with a small memory overhead. To improve the measurement accuracy, most sketch-based schemes separate elephant flows from mouse flows to accommodate the skewed network traffic. However, the increased algorithmic complexity often results in a sacrifice of measurement throughput. In addition, some improved sketches may be over-reliant on the skewed distribution of traffic, which results in unstable accuracy. To this end, a novel sketch, called Air Sketch, is proposed in this paper. It treats flows of different sizes as air with different temperatures. Meanwhile, a deterministic replacement strategy is applied to elephant flows. In order to improve throughput, an asymmetric insertion and query algorithm with a global hash is designed. The performance of Air Sketch is evaluated using real traffic transaction datasets, anonymized internet traces, and synthetic Zipf datasets. The experimental results demonstrate that Air Sketch can outperform the best typical measurement methods by up to 27 times in flow size measurement and up to 40 times in elephant flow detection. Additionally, Air Sketch achieves high accuracy and stability while achieving high insertion and query throughput. Full article

Background/Objectives: Elephant endotheliotropic herpesvirus (EEHV) causes lethal hemorrhagic disease (HD) in Asian and African elephants in human care and the wild. It is the leading cause of death for young Asian elephants in North American and European zoos despite sensitive diagnostic tests and improved treatments. Thus, there is a critical need to develop an effective vaccine to prevent severe illness and reduce mortality from EEHV-HD. We generated a multi-antigenic EEHV mRNA vaccine to address this need that encodes the EEHV1A-subtype glycoproteins gB, gH, gL, and gO. These conserved proteins are the entry machinery for several herpesviruses in the betaherpesvirus subfamily and elicit humoral and cellular immunity in naturally infected elephants. Methods: Outbred CD-1 mice were vaccinated with two doses of an mRNA vaccine comprising modified EEHV1A gB, gH, gL, and gO mRNAs encapsulated into lipid nanoparticles. Humoral and T-cell immunity was assessed three weeks after the first dose or three weeks after the booster dose using luciferase immunoprecipitation system assays and flow cytometry, respectively. Results: The CD-1 mice vaccinated once had detectable antibody titers against gB, gH, and gL that increased significantly three weeks after a booster dose. Activated CD4⁺ and CD8⁺ T cells secreting cytokines associated with a T_H1 response were induced against all four glycoproteins. No adverse effects were observed following one or two doses of the vaccine. Conclusions: We found that gB, gH, gL, and gO as a multivalent vaccine stimulated robust humoral and cell-mediated immunity. This is a critical step for moving this candidate EEHV1A mRNA vaccine into clinical trials in Asian elephants. Full article

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments. Full article

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. Existing methods either use large-size counters, i.e., 20 bit, to prevent overflows when recording flow sizes or require significant space overhead to measure the sizes of all flows. Thus, we adopt a novel strategy, called count-with-uth-level-sampling, in this paper, to find top-k elephant flows in limited resource environments. Moreover, the proposed algorithm, called MSLCFinder, incurs lightweight counter and uth-level multi-sampling with small, constant processing for millions of flows. Experimental results show that MSLCFinder can achieve more than 97% precision with an extremely limited hardware resource. Compared to the state-of-the-art, our method realizes the statistics and filtering of millions of data streams with less memory. Full article

Savannah elephant populations have been severely reduced and fragmented throughout its remaining range. In general, however, there is limited information regarding their genetic status, which is essential knowledge for conservation. We investigated patterns of genetic variation in savannah elephants from the Greater Kruger Biosphere, with a focus on those in previously unstudied nature reserves adjacent to Kruger National Park, using dung samples from 294 individuals and 18 microsatellites. The results of genetic structure analyses using several different methods of ordination and Bayesian clustering strongly suggest that elephants throughout the Greater Kruger National Park (GKNP) constitute a single population. No evidence of a recent genetic bottleneck was detected using three moment-based approaches and two coalescent likelihood methods. The apparent absence of a recent genetic bottleneck associated with the known early 1900s demographic bottleneck may result from a combination of rapid post-bottleneck population growth, immigration and long generation time. Point estimates of contemporary effective population size (N_e) for the GKNP were ~ 500–700, that is, at the low end of the range of N_e values that have been proposed for maintaining evolutionary potential and the current ratio of N_e to census population size (N_c) may be quite low (<0.1). This study illustrates the difficulties in assessing the impacts on N_e in populations that have suffered demographic crashes but have recovered rapidly and received gene flow, particularly in species with long generation times in which genetic time lags are longer. This work provides a starting point and baseline information for genetic monitoring of the GKNP elephants. Full article

The main advantage of software defined networking (SDN) is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three main modules in the SDN controller for a robust and agile defense (RAD) system against network anomalies: a traffic analyzer, a traffic engineer, and a rule manager. RAD provides reactive flow rule generation to control traffic while detecting network failures, anomalies, high traffic volume (elephant flows), and attacks. The traffic analyzer identifies elephant flows, traffic anomalies, and attacks based on attack signatures and network monitoring. The traffic engineer module measures network utilization and delay in order to determine the best path for multi-dimensional routing and load balancing under any circumstances. Finally, the rule manager generates and installs a flow rule for the selected best path to control traffic. We implement the proposed RAD system based on Floodlight, an open source project for the SDN controller. We evaluate our system using simulation with and without the aforementioned RAD modules. Experimental results show that our approach is both practical and feasible, and can successfully augment an existing SDN controller in terms of agility, robustness, and efficiency, even in the face of link failures, attacks, and elephant flows. Full article