A System Architecture for Real-time Anomaly Detection in Large-scale NFV Systems

Virtualization as a key IT technology has developed to a predominant model in data centers in recent years. The flexibility regarding scaling-out and migration of virtual machines for seamless maintenance has enabled a new level of continuous operation and changed service provisioning significantly. Meanwhile, services from domains striving for highest possible availability – e.g. from the telecommunications domain – are adopting this approach as well and are investing significant efforts into the development of Network Function Virtualization (NFV). However, the availability requirements for such infrastructures are much higher than typical for IT services built upon standard software with off-the-shelf hardware. They require sophisticated methods and mechanisms for fast detection and recovery of failures. This paper presents a set of methods and an implemented prototype for anomaly detection in cloud-based infrastructures with specific focus on the deployment of virtualized network functions. The framework is built upon OpenStack, which is the current de-facto standard of open-source cloud software and aims at increasing the availability and fault tolerance level by providing an extensive monitoring and analysis pipeline able to detect failures or degraded performance in real-time. The indicators for anomalies are created using supervised and non-supervised classification methods and preliminary experimental measurements showed a high percentage of correctly identified anomaly situations. After a successful failure detection, a set of pre-defined countermeasures is activated in order to mask or repair outages or situations with degraded performance

Automated Anomaly Detection in Virtualized Services Using Deep Packet Inspection

Virtualization technologies have proven to be important drivers for the fast and cost-efficient development and deployment of services. While the benefits are tremendous, there are many challenges to be faced when developing or porting services to virtualized infrastructure. Especially critical applications like Virtualized Network Functions must meet high requirements in terms of reliability and resilience. An important tool when meeting such requirements is detecting anomalous system components and recovering the anomaly before it turns into a fault and subsequently into a failure visible to the client. Anomaly detection for virtualized services relies on collecting system metrics that represent the normal operation state of every component and allow the usage of machine learning algorithms to automatically build models representing such state. This paper presents an approach for collecting service-layer metrics while treating services as black-boxes. This allows service providers to implement anomaly detection on the application layer without the need to modify third-party software. Deep Packet Inspection is used to analyse the traffic of virtual machines on the hypervisor layer, producing both generic and protocol-specific communication metrics. An evaluation shows that the resulting metrics represent the normal operation state of an example Virtualized Network Function and are therefore a valuable contribution to automatic anomaly detection in virtualized services

Speciation of arsenic in sulfidic waters

Formation constants for thioarsenite species have been determined in dilute solutions at 25°C, ΣH(2)S from 10(-7.5 )to 10(-3.0 )M, ΣAs from 10(-5.6 )to 10(-4.8 )M, and pH 7 and 10. The principal inorganic arsenic species in anoxic aquatic systems are arsenite, As(OH)(3)(0), and a mononuclear thioarsenite with an S/As ratio of 3:1. Thioarsenic species with S/As ratios of 1 : 1,2 : 1, and 4 : 1 are lesser components in sulfidic solutions that might be encountered in natural aquatic environments. Thioarsenites dominate arsenic speciation at sulfide concentrations > 10(-4.3 )M at neutral pH. Conversion from neutral As(OH)(3)(0 )to anionic thioarsenite species may regulate the transport and fate of arsenic in sulfate-reducing environments by governing sorption and mineral precipitation reactions

Body size and vocalization in primates and carnivores

A fundamental assumption in bioacoustics is that large animals tend to produce vocalizations with lower frequencies than small animals. This inverse relationship between body size and vocalization frequencies is widely considered to be foundational in animal communication, with prominent theories arguing that it played a critical role in the evolution of vocal communication, in both production and perception. A major shortcoming of these theories is that they lack a solid empirical foundation: rigorous comparisons between body size and vocalization frequencies remain scarce, particularly among mammals. We address this issue here in a study of body size and vocalization frequencies conducted across 91 mammalian species, covering most of the size range in the orders Primates (n = 50; ~0.11–120 Kg) and Carnivora (n = 41; ~0.14–250 Kg). We employed a novel procedure designed to capture spectral variability and standardize frequency measurement of vocalization data across species. The results unequivocally demonstrate strong inverse relationships between body size and vocalization frequencies in primates and carnivores, filling a long-standing gap in mammalian bioacoustics and providing an empirical foundation for theories on the adaptive function of call frequency in animal communication

Gaseous mercury flux from salt marshes is mediated by solar radiation and temperature

Salt marshes are ecologically sensitive ecosystems where mercury (Hg) methylation and biomagnification can occur. Understanding the mechanisms controlling gaseous Hg flux from salt marshes is important to predict the retention of Hg in coastal wetlands and project the impact of environmental change on the global Hg cycle. We monitored Hg flux from a remote salt marsh over 9 days which included three cloudless days and a 4 mm rainfall event. We observed a cyclical diel relationship between Hg flux and solar radiation. When measurements at the same irradiance intensity are considered, Hg flux was greater in the evening when the sediment was warm than in the morning when the sediment was cool. This is evidence to suggest that both solar radiation and sediment temperature directly influence the rate of Hg(II) photoreduction in salt marshes. Hg flux could be predicted from solar radiation and sediment temperature in sub-datasets collected during cloudless days (R2 = 0.99), and before (R2 = 0.97) and after (R2 = 0.95) the rainfall event, but the combined dataset could not account for the lower Hg flux observed after the rainfall event that is in contrast to greater Hg flux from soils after rainfall events

A Cervid Vocal Fold Model Suggests Greater Glottal Efficiency in Calling at High Frequencies

Male Rocky Mountain elk (Cervus elaphus nelsoni) produce loud and high fundamental frequency bugles during the mating season, in contrast to the male European Red Deer (Cervus elaphus scoticus) who produces loud and low fundamental frequency roaring calls. A critical step in understanding vocal communication is to relate sound complexity to anatomy and physiology in a causal manner. Experimentation at the sound source, often difficult in vivo in mammals, is simulated here by a finite element model of the larynx and a wave propagation model of the vocal tract, both based on the morphology and biomechanics of the elk. The model can produce a wide range of fundamental frequencies. Low fundamental frequencies require low vocal fold strain, but large lung pressure and large glottal flow if sound intensity level is to exceed 70 dB at 10 m distance. A high-frequency bugle requires both large muscular effort (to strain the vocal ligament) and high lung pressure (to overcome phonation threshold pressure), but at least 10 dB more intensity level can be achieved. Glottal efficiency, the ration of radiated sound power to aerodynamic power at the glottis, is higher in elk, suggesting an advantage of high-pitched signaling. This advantage is based on two aspects; first, the lower airflow required for aerodynamic power and, second, an acoustic radiation advantage at higher frequencies. Both signal types are used by the respective males during the mating season and probably serve as honest signals. The two signal types relate differently to physical qualities of the sender. The low-frequency sound (Red Deer call) relates to overall body size via a strong relationship between acoustic parameters and the size of vocal organs and body size. The high-frequency bugle may signal muscular strength and endurance, via a ‘vocalizing at the edge’ mechanism, for which efficiency is critical