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Cloudburst

About: Cloudburst is a research topic. Over the lifetime, 117 publications have been published within this topic receiving 1763 citations.


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Journal ArticleDOI
TL;DR: CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics.
Abstract: Motivation: Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor readmapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics. It is modeled after the short read-mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. Results: CloudBurst’s running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments. Using a larger remote compute cloud with 96 cores, CloudBurst improved performance by >100-fold, reducing the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome. Availability: CloudBurst is available open-source as a model for parallelizing algorithms with MapReduce at http://cloudburst

669 citations

Journal ArticleDOI
TL;DR: Empirical results show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.
Abstract: Function-as-a-Service (FaaS) platforms and "serverless" cloud computing are becoming increasingly popular. Current FaaS offerings are targeted at stateless functions that do minimal I/O and communication. We argue that the benefits of serverless computing can be extended to a broader range of applications and algorithms. We present the design and implementation of Cloudburst, a stateful FaaS platform that provides familiar Python programming with low-latency mutable state and communication, while maintaining the autoscaling benefits of serverless computing. Cloudburst accomplishes this by leveraging Anna, an autoscaling key-value store, for state sharing and overlay routing combined with mutable caches co-located with function executors for data locality. Performant cache consistency emerges as a key challenge in this architecture. To this end, Cloudburst provides a combination of lattice-encapsulated state and new definitions and protocols for distributed session consistency. Empirical results on benchmarks and diverse applications show that Cloudburst makes stateful functions practical, reducing the state-management overheads of current FaaS platforms by orders of magnitude while also improving the state of the art in serverless consistency.

128 citations

Journal ArticleDOI
TL;DR: An extensive and in-depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters.

120 citations

Journal ArticleDOI
TL;DR: In this paper, a triangular hydrograph has been transformed into a storm hydrographer for better understanding of the duration of the storm in the Ladakh region of India.
Abstract: Leh and surrounding region of the Ladakh mountain range in the trans-Himalaya experienced multiple cloudbursts and associated flash floods during August 4–6, 2010. However, 12.8 mm/day rainfall recorded at the nearest meteorological station at Leh did not corroborate with the flood severity. For better understanding of this event, hydrological analysis and atmospheric modeling are carried out in tandem. Two small catchments (<3 km2) were studied along the stream continuum to assess the flood characteristics to identify the cloudburst impact zones. Peak flood discharges were estimated close to the head wall region and at the catchment outlet of the Leh town and the Sabu eastern tributary catchments. Storm runoff depth is estimated by developing a triangular hydrograph by using the known time base of the flood hydrograph. This triangular hydrographs have been transformed further into storm hydrographs to gain a better understanding of the storm duration by using the dimensionless hydrograph method at selected cross sections. Storm duration is estimated by using the relationship between time to peak and time of concentration of the catchment. The peak flood estimates ranged from 122(±35 %) m3/s for Leh town catchment (2.393 km2), 545(±35 %) m3/s for Sabu eastern tributary catchment (2.831 km2) to 1,070(±35 %) m3/sec for Sabu catchment (64.95 km2). To assess the atmospheric processes associated with this event, a triple nest simulation (27, 9 and 3 km) is performed using Advanced Research Weather Research and Forecasting (WRF) modeling system. The simulation does show the evolution of the event from August 4 to 6, 2010. Observation constraints, orographic responses, etc. make such analysis complex at such scale. Independent estimate by the atmospheric process model and the hydrological method shows the storm depth of 70 mm and 91.8(±35 %) mm, respectively, in catchment scale. Hydrological evaluation further refined the spatial and temporal extents of the cloudbursts in the respective catchments with an estimated storm depth of 209(±35 %) mm in 11.9 min and 320(±35 %) in 8.8 min occurring in an area of 0.842–1.601 km2, respectively. This study shows that the insight developed on the cloudburst phenomena by the atmospheric and the hydrological modeling is hugely constrained by the spatial and temporal scales of data used for the analysis. Apart from this, study also highlighted the regular occurrence of cloudburst events over this region in the recent past. Most of such events go unreported due to lack of monitoring mechanisms in the region and weaken our ability to understand these events in complete perspective.

103 citations

Journal ArticleDOI
TL;DR: In this article, the authors synthesize the available information and research on cloudburst events and try to define it based on associated dynamics, thermodynamics and physical processes leading to a cloudburst event.

99 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
20218
202013
20196
20186
201713