91 resultados para CLOUD CORES


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The Future Communication Architecture for Mobile Cloud Services: Mobile Cloud Networking (MCN) is a EU FP7 Large-scale Integrating Project (IP) funded by the European Commission. MCN project was launched in November 2012 for the period of 36 month. In total top-tier 19 partners from industry and academia commit to jointly establish the vision of Mobile Cloud Networking, to develop a fully cloud-based mobile communication and application platform.

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Abstract. Ancient Lake Ohrid is a steep-sided, oligotrophic, karst lake that was tectonically formed most likely within the Pliocene and often referred to as a hotspot of endemic biodiversity. This study aims on tracing significant lake level fluctuations at Lake Ohrid using high-resolution acoustic data in combination with lithological, geochemical, and chronological information from two sediment cores recovered from sub-aquatic terrace levels at ca. 32 and 60m water depth. According to our data, significant lake level fluctuations with prominent lowstands of ca. 60 and 35m below the present water level occurred during Marine Isotope Stage (MIS) 6 and MIS 5, respectively. The effect of these lowstands on biodiversity in most coastal parts of the lake is negligible, due to only small changes in lake surface area, coastline, and habitat. In contrast, biodiversity in shallower areas was more severely affected due to disconnection of today sublacustrine springs from the main water body. Multichannel seismic data from deeper parts of the lake clearly image several clinoform structures stacked on top of each other. These stacked clinoforms indicate significantly lower lake levels prior to MIS 6 and a stepwise rise of water level with intermittent stillstands since its existence as water-filled body, which might have caused enhanced expansion of endemic species within Lake Ohrid.

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Virtualisation of cellular networks can be seen as a way to significantly reduce the complexity of processes, required nowadays to provide reliable cellular networks. The Future Communication Architecture for Mobile Cloud Services: Mobile Cloud Networking (MCN) is a EU FP7 Large-scale Integrating Project (IP) funded by the European Commission that is focusing on cloud computing concepts to achieve virtualisation of cellular networks. It aims at the development of a fully cloud-based mobile communication and application platform, or more specifically, it aims to investigate, implement and evaluate the technological foundations for the mobile communication system of Long Term Evolution (LTE), based on Mobile Network plus Decentralized Computing plus Smart Storage offered as one atomic service: On-Demand, Elastic and Pay-As-You-Go. This paper provides a brief overview of the MCN project and discusses the challenges that need to be solved.

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For atmospheric CO2 reconstructions using ice cores, the technique to release the trapped air from the ice samples is essential for the precision and accuracy of the measurements. We present here a new dry extraction technique in combination with a new gas analytical system that together show significant improvements with respect to current systems. Ice samples (3–15 g) are pulverised using a novel centrifugal ice microtome (CIM) by shaving the ice in a cooled vacuum chamber (−27 °C) in which no friction occurs due to the use of magnetic bearings. Both, the shaving principle of the CIM and the use of magnetic bearings have not been applied so far in this field. Shaving the ice samples produces finer ice powder and releases a minimum of 90% of the trapped air compared to 50%–70% when needle crushing is employed. In addition, the friction-free motion with an optimized design to reduce contaminations of the inner surfaces of the device result in a reduced system offset of about 2.0 ppmv compared to 4.9 ppmv. The gas analytical part shows a higher precision than the corresponding part of our previous system by a factor of two, and all processes except the loading and cleaning of the CIM now run automatically. Compared to our previous system, the complete system shows a 3 times better measurement reproducibility of about 1.1 ppmv (1 σ) which is similar to the best reproducibility of other systems applied in this field. With this high reproducibility, no replicate measurements are required anymore for most future measurement campaigns resulting in a possible output of 12–20 measurements per day compared to a maximum of 6 with other systems.

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The Toba eruption that occurred some 74 ka ago in Sumatra, Indonesia, is among the largest volcanic events on Earth over the last 2 million years. Tephra from this eruption has been spread over vast areas in Asia, where it constitutes a major time marker close to the Marine Isotope Stage 4/5 boundary. As yet, no tephra associated with Toba has been identified in Greenland or Antarctic ice cores. Based on new accurate dating of Toba tephra and on accurately dated European stalagmites, the Toba event is known to occur between the onsets of Greenland interstadials (GI) 19 and 20. Furthermore, the existing linking of Greenland and Antarctic ice cores by gas records and by the bipolar seesaw hypothesis suggests that the Antarctic counterpart is situated between Antarctic Isotope Maxima (AIM) 19 and 20. In this work we suggest a direct synchronization of Greenland (NGRIP) and Antarctic (EDML) ice cores at the Toba eruption based on matching of a pattern of bipolar volcanic spikes. Annual layer counting between volcanic spikes in both cores allows for a unique match. We first demonstrate this bipolar matching technique at the already synchronized Laschamp geomagnetic excursion (41 ka BP) before we apply it to the suggested Toba interval. The Toba synchronization pattern covers some 2000 yr in GI-20 and AIM-19/20 and includes nine acidity peaks that are recognized in both ice cores. The suggested bipolar Toba synchronization has decadal precision. It thus allows a determination of the exact phasing of inter-hemispheric climate in a time interval of poorly constrained ice core records, and it allows for a discussion of the climatic impact of the Toba eruption in a global perspective. The bipolar linking gives no support for a long-term global cooling caused by the Toba eruption as Antarctica experiences a major warming shortly after the event. Furthermore, our bipolar match provides a way to place palaeo-environmental records other than ice cores into a precise climatic context.

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The important active and passive role of mineral dust aerosol in the climate and the global carbon cycle over the last glacial/interglacial cycles has been recognized. However, little data on the most important aeolian dust-derived biological micronutrient, iron (Fe), has so far been available from ice-cores from Greenland or Antarctica. Furthermore, Fe deposition reconstructions derived from the palaeoproxies particulate dust and calcium differ significantly from the Fe flux data available. The ability to measure high temporal resolution Fe data in polar ice-cores is crucial for the study of the timing and magnitude of relationships between geochemical events and biological responses in the open ocean. This work adapts an existing flow injection analysis (FIA) methodology for low-level trace Fe determinations with an existing glaciochemical analysis system, continuous flow analysis (CFA) of ice-cores. Fe-induced oxidation of N,N′-dimethyl-p-pheylenediamine (DPD) is used to quantify the biologically more important and easily leachable Fe fraction released in a controlled digestion step at pH ∼1.0. The developed method was successfully applied to the determination of labile Fe in ice-core samples collected from the Antarctic Byrd ice-core and the Greenland Ice-Core Project (GRIP) ice-core.

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Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client’s site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.

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Phosphorus (P) is an essential macronutrient for all living organisms. Phosphorus is often present in nature as the soluble phosphate ion PO43– and has biological, terrestrial, and marine emission sources. Thus PO43– detected in ice cores has the potential to be an important tracer for biological activity in the past. In this study a continuous and highly sensitive absorption method for detection of dissolved reactive phosphorus (DRP) in ice cores has been developed using a molybdate reagent and a 2-m liquid waveguide capillary cell (LWCC). DRP is the soluble form of the nutrient phosphorus, which reacts with molybdate. The method was optimized to meet the low concentrations of DRP in Greenland ice, with a depth resolution of approximately 2 cm and an analytical uncertainty of 1.1 nM (0.1 ppb) PO43–. The method has been applied to segments of a shallow firn core from Northeast Greenland, indicating a mean concentration level of 2.74 nM (0.26 ppb) PO43– for the period 1930–2005 with a standard deviation of 1.37 nM (0.13 ppb) PO43– and values reaching as high as 10.52 nM (1 ppb) PO43–. Similar levels were detected for the period 1771–1823. Based on impurity abundances, dust and biogenic particles were found to be the most likely sources of DRP deposited in Northeast Greenland.

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When drilling ice cores deeper than ∼100 m, drill liquid is required to maintain ice-core quality and to limit borehole closure. Due to high-pressure air bubbles in the ice, the ice core can crack during drilling and core retrieval, typically at 600–1200 m depth in Greenland. Ice from this 'brittle zone' can be contaminated by drill liquid as it seeps through cracks into the core. Continuous flow analysis (CFA) systems are routinely used to analyse ice for chemical impurities, so the detection of drill liquid is important for validating accurate measurements and avoiding potential instrument damage. An optical detector was constructed to identify drill liquid in CFA tubing by ultraviolet absorption spectroscopy at a wavelength of 290 nm. The set-up was successfully field-tested in the frame of the NEEM ice-core drilling project in Greenland. A total of 27 cases of drill liquid contamination were identified during the analysis of 175 m of brittle zone ice. The analyses most strongly affected by drill liquid contamination include insoluble dust particles, electrolytic conductivity, ammonium, hydrogen peroxide and sulphate. This method may also be applied to other types of drill liquid used at other drill sites.

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Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm.

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This paper addresses the novel notion of offering a radio access network as a service. Its components may be instantiated on general purpose platforms with pooled resources (both radio and hardware ones) dimensioned on-demand, elastically and following the pay-per-use principle. A novel architecture is proposed that supports this concept. The architecture's success is in its modularity, well-defined functional elements and clean separation between operational and control functions. By moving much processing traditionally located in hardware for computation in the cloud, it allows the optimisation of hardware utilization and reduction of deployment and operation costs. It enables operators to upgrade their network as well as quickly deploy and adapt resources to demand. Also, new players may easily enter the market, permitting a virtual network operator to provide connectivity to its users.

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Cloud Computing is an enabler for delivering large-scale, distributed enterprise applications with strict requirements in terms of performance. It is often the case that such applications have complex scaling and Service Level Agreement (SLA) management requirements. In this paper we present a simulation approach for validating and comparing SLA-aware scaling policies using the CloudSim simulator, using data from an actual Distributed Enterprise Information System (dEIS). We extend CloudSim with concurrent and multi-tenant task simulation capabilities. We then show how different scaling policies can be used for simulating multiple dEIS applications. We present multiple experiments depicting the impact of VM scaling on both datacenter energy consumption and dEIS performance indicators.

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Water stable isotope ratios and net snow accumulation in ice cores are commonly interpreted as temperature or precipitation proxies. However, only in a few cases has a direct calibration with instrumental data been attempted. In this study we took advantage of the dense network of observations in the European Alpine region to rigorously test the relationship of the annual and seasonal resolved proxy data from two highly resolved ice cores with local temperature and precipitation. We focused on the time period 1961–2001 with the highest amount and quality of meteorological data and the minimal uncertainty in ice core dating (±1 year). The two ice cores were retrieved from the Fiescherhorn glacier (northern Alps, 3900 m a.s.l.), and Grenzgletscher (southern Alps, 4200 m a.s.l.). A parallel core from the Fiescherhorn glacier allowed assessing the reproducibility of the ice core proxy data. Due to the orographic barrier, the two flanks of the Alpine chain are affected by distinct patterns of precipitation. The different location of the two glaciers therefore offers a unique opportunity to test whether such a specific setting is reflected in the proxy data. On a seasonal scale a high fraction of δ18O variability was explained by the seasonal cycle of temperature (~60% for the ice cores, ~70% for the nearby stations of the Global Network of Isotopes in Precipitation – GNIP). When the seasonality is removed, the correlations decrease for all sites, indicating that factors other than temperature such as changing moisture sources and/or precipitation regimes affect the isotopic signal on this timescale. Post-depositional phenomena may additionally modify the ice core data. On an annual scale, the δ18O/temperature relationship was significant at the Fiescherhorn, whereas for Grenzgletscher this was the case only when weighting the temperature with precipitation. In both cases the fraction of interannual temperature variability explained was ~20%, comparable to the values obtained from the GNIP stations data. Consistently with previous studies, we found an altitude effect for the δ18O of −0.17‰/100 m for an extended elevation range combining data of the two ice core sites and four GNIP stations. Significant correlations between net accumulation and precipitation were observed for Grenzgletscher during the entire period of investigation, whereas for Fiescherhorn this was the case only for the less recent period (1961–1977). Local phenomena, probably related to wind, seem to partly disturb the Fiescherhorn accumulation record. Spatial correlation analysis shows the two glaciers to be influenced by different precipitation regimes, with the Grenzgletscher reflecting the characteristic precipitation regime south of the Alps and the Fiescherhorn accumulation showing a pattern more closely linked to northern Alpine stations.

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The near-real time retrieval of low stratiform cloud (LSC) coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR) data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV). Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS) cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.