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Journal articles on the topic 'AWS Serverless'
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Ratkov, Aleksandar. "DIZAJN SERVERLESS WEB APLIKACIJA NA AMAZON PLATFORMI." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 34, no. 11 (November 3, 2019): 2009–11. http://dx.doi.org/10.24867/05be16ratkov.
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Risco, Sebastián, and Germán Moltó. "GPU-Enabled Serverless Workflows for Efficient Multimedia Processing." Applied Sciences 11, no. 4 (February 5, 2021): 1438. http://dx.doi.org/10.3390/app11041438.
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Serverless computing has introduced scalable event-driven processing in Cloud infrastructures. However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications. To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources. This has been achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs. In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of functions. These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing. To assess the developed open-source framework, we executed a case study for efficient serverless video processing. The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services. This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.
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Roobini, M. S., Selvasurya Sampathkumar, Shaik Khadar Basha, and Anitha Ponraj. "Serverless Computing Using Amazon Web Services." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3581–85. http://dx.doi.org/10.1166/jctn.2020.9235.
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In the last decade cloud computing transformed the way in which we build applications. The boom in cloud computing helped to develop new software design and architecture. Helping the developers to focus more on the business logic than the infrastructure. FaaS (function as a service) compute model it gave developers to concentrate only on the application code and rest of the factors will be taken care by the cloud provider. Here we present a serverless architecture of a web application built using AWS services and provide detail analysis of lambda function and micro service software design implemented using these AWS services.
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Pogiatzis, Antreas, and Georgios Samakovitis. "An Event-Driven Serverless ETL Pipeline on AWS." Applied Sciences 11, no. 1 (December 28, 2020): 191. http://dx.doi.org/10.3390/app11010191.
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This work presents an event-driven Extract, Transform, and Load (ETL) pipeline serverless architecture and provides an evaluation of its performance over a range of dataflow tasks of varying frequency, velocity, and payload size. We design an experiment while using generated tabular data throughout varying data volumes, event frequencies, and processing power in order to measure: (i) the consistency of pipeline executions; (ii) reliability on data delivery; (iii) maximum payload size per pipeline; and, (iv) economic scalability (cost of chargeable tasks). We run 92 parameterised experiments on a simple AWS architecture, thus avoiding any AWS-enhanced platform features, in order to allow for unbiased assessment of our model’s performance. Our results indicate that our reference architecture can achieve time-consistent data processing of event payloads of more than 100 MB, with a throughput of 750 KB/s across four event frequencies. It is also observed that, although the utilisation of an SQS queue for data transfer enables easy concurrency control and data slicing, it becomes a bottleneck on large sized event payloads. Finally, we develop and discuss a candidate pricing model for our reference architecture usage.
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Muller, Lisa, Christos Chrysoulas, Nikolaos Pitropakis, and Peter J. Barclay. "A Traffic Analysis on Serverless Computing Based on the Example of a File Upload Stream on AWS Lambda." Big Data and Cognitive Computing 4, no. 4 (December 10, 2020): 38. http://dx.doi.org/10.3390/bdcc4040038.
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The shift towards microservisation which can be observed in recent developments of the cloud landscape for applications has led towards the emergence of the Function as a Service (FaaS) concept, also called Serverless. This term describes the event-driven, reactive programming paradigm of functional components in container instances, which are scaled, deployed, executed and billed by the cloud provider on demand. However, increasing reports of issues of Serverless services have shown significant obscurity regarding its reliability. In particular, developers and especially system administrators struggle with latency compliance. In this paper, following a systematic literature review, the performance indicators influencing traffic and the effective delivery of the provider’s underlying infrastructure are determined by carrying out empirical measurements based on the example of a File Upload Stream on Amazon’s Web Service Cloud. This popular example was used as an experimental baseline in this study, based on different incoming request rates. Different parameters were used to monitor and evaluate changes through the function’s logs. It has been found that the so-called Cold-Start, meaning the time to provide a new instance, can increase the Round-Trip-Time by 15%, on average. Cold-Start happens after an instance has not been called for around 15 min, or after around 2 h have passed, which marks the end of the instance’s lifetime. The research shows how the numbers have changed in comparison to earlier related work, as Serverless is a fast-growing field of development. Furthermore, emphasis is given towards future research to improve the technology, algorithms, and support for developers.
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Giménez-Alventosa, V., Germán Moltó, and Miguel Caballer. "A framework and a performance assessment for serverless MapReduce on AWS Lambda." Future Generation Computer Systems 97 (August 2019): 259–74. http://dx.doi.org/10.1016/j.future.2019.02.057.
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Andi, Hari Krishnan. "Analysis of Serverless Computing Techniques in Cloud Software Framework." September 2021 3, no. 3 (August 20, 2021): 221–34. http://dx.doi.org/10.36548/jismac.2021.3.004.
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This paper describes briefly about the concept of serverless cloud computing model, its usage in IT industries and its benefits. In the traditional model the developer is responsible for resource allocation, managing servers and owning of servers, and it included three models based upon the service such as IaaS, PaaS and SaaS. In IaaS (Infrastructure as a Service) the content storage and accessing of network is carried out by the cloud provider, SaaS (Software as a Service) here different software’s are provided to the user as a service, PaaS (Platform as a Service), the developer gets access to certain services for carrying out organizing process and run it accordingly. In serverless cloud computing, the developer need not worry about owning, management, and maintenance of servers as it is carried out by the cloud service provider. Hence by using this model, the time that is needed for a system to reach the market is very much reduced and is cost effective. Serverless architecture includes three categories namely, AWS Lambda, Azure, and Google cloud. It also includes certain challenges such as it cannot be used in the case where a process takes longer time to run and it is discussed below in this paper.
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Malawski, Maciej, Adam Gajek, Adam Zima, Bartosz Balis, and Kamil Figiela. "Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions." Future Generation Computer Systems 110 (September 2020): 502–14. http://dx.doi.org/10.1016/j.future.2017.10.029.
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Bebortta, Sujit, Saneev Kumar Das, Meenakshi Kandpal, Rabindra Kumar Barik, and Harishchandra Dubey. "Geospatial Serverless Computing: Architectures, Tools and Future Directions." ISPRS International Journal of Geo-Information 9, no. 5 (May 7, 2020): 311. http://dx.doi.org/10.3390/ijgi9050311.
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Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.
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Lee, Soohyun, Jeremy Johnson, Carl Vitzthum, Koray Kırlı, Burak H. Alver, and Peter J. Park. "Tibanna: software for scalable execution of portable pipelines on the cloud." Bioinformatics 35, no. 21 (May 11, 2019): 4424–26. http://dx.doi.org/10.1093/bioinformatics/btz379.
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Abstract Summary We introduce Tibanna, an open-source software tool for automated execution of bioinformatics pipelines on Amazon Web Services (AWS). Tibanna accepts reproducible and portable pipeline standards including Common Workflow Language (CWL), Workflow Description Language (WDL) and Docker. It adopts a strategy of isolation and optimization of individual executions, combined with a serverless scheduling approach. Pipelines are executed and monitored using local commands or the Python Application Programming Interface (API) and cloud configuration is automatically handled. Tibanna is well suited for projects with a range of computational requirements, including those with large and widely fluctuating loads. Notably, it has been used to process terabytes of data for the 4D Nucleome (4DN) Network. Availability and implementation Source code is available on GitHub at https://github.com/4dn-dcic/tibanna. Supplementary information Supplementary data are available at Bioinformatics online.
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Paul Millar, A., Olufemi Adeyemi, Vincent Garonne, Dmitry Litvintsev, Tigran Mkrtchyan, Albert Rossi, Marina Sahakyan, and Jürgen Starek. "Storage events: distributed users, federation and beyond." EPJ Web of Conferences 214 (2019): 04035. http://dx.doi.org/10.1051/epjconf/201921404035.
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For federated storage to work well, some knowledge from each storage system must exist outside that system, regardless of the use case. This is needed to allow coordinated activity; e.g., executing analysis jobs on worker nodes with good accessibility to the data. Currently, this is achieved by clients notifying central services of activity; e.g., a client notifies a replica catalogue after an upload. Unfortunately, this forces end users to use bespoke clients. It also forces clients to wait for asynchronous activities to finish. dCache provides an alternative approach: storage events. In this approach the storage systems (rather than the clients) become the coordinating service, notifying interested parties of key events. At DESY, we are investigating storage events along with Apache OpenWhisk and Kubernetes to build a "serverless" cloud, similar to AWS Lambda or Google Cloud Functions, for photon science use cases. Storage events are more generally useful: catalogues are notified whenever data is uploaded or delete, tape becomes more efficient because analysis can start immediately after the data is on disk, caches can be "smart" fetching new datasets preemptively. In this paper we will present work within dCache to support a new event-based interface, with which these and other use cases become more efficient.
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Risco, Sebastián, Germán Moltó, Diana M. Naranjo, and Ignacio Blanquer. "Serverless Workflows for Containerised Applications in the Cloud Continuum." Journal of Grid Computing 19, no. 3 (July 13, 2021). http://dx.doi.org/10.1007/s10723-021-09570-2.
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AbstractThis paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.
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Mao, Zhitao, Ruoyu Wang, Haoran Li, Yixin Huang, Qiang Zhang, Xiaoping Liao, and Hongwu Ma. "ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database." Nucleic Acids Research, April 30, 2022. http://dx.doi.org/10.1093/nar/gkac288.
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Abstract Cellular regulation is inherently complex, and one particular cellular function is often controlled by a cascade of different types of regulatory interactions. For example, the activity of a transcription factor (TF), which regulates the expression level of downstream genes through transcriptional regulation, can be regulated by small molecules through compound–protein interactions. To identify such complex regulatory cascades, traditional relational databases require ineffective additional operations and are computationally expensive. In contrast, graph databases are purposefully developed to execute such deep searches efficiently. Here, we present ERMer (E. coli Regulation Miner), the first cloud platform for mining the regulatory landscape of Escherichia coli based on graph databases. Combining the AWS Neptune graph database, AWS lambda function, and G6 graph visualization engine enables quick search and visualization of complex regulatory cascades/patterns. Users can also interactively navigate the E. coli regulatory landscape through ERMer. Furthermore, a Q&A module is included to showcase the power of graph databases in answering complex biological questions through simple queries. The backend graph model can be easily extended as new data become available. In addition, the framework implemented in ERMer can be easily migrated to other applications or organisms. ERMer is available at https://ermer.biodesign.ac.cn/.
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Mao, Zhitao, Ruoyu Wang, Haoran Li, Yixin Huang, Qiang Zhang, Xiaoping Liao, and Hongwu Ma. "ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database." Nucleic Acids Research, April 30, 2022. http://dx.doi.org/10.1093/nar/gkac288.
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Abstract Cellular regulation is inherently complex, and one particular cellular function is often controlled by a cascade of different types of regulatory interactions. For example, the activity of a transcription factor (TF), which regulates the expression level of downstream genes through transcriptional regulation, can be regulated by small molecules through compound–protein interactions. To identify such complex regulatory cascades, traditional relational databases require ineffective additional operations and are computationally expensive. In contrast, graph databases are purposefully developed to execute such deep searches efficiently. Here, we present ERMer (E. coli Regulation Miner), the first cloud platform for mining the regulatory landscape of Escherichia coli based on graph databases. Combining the AWS Neptune graph database, AWS lambda function, and G6 graph visualization engine enables quick search and visualization of complex regulatory cascades/patterns. Users can also interactively navigate the E. coli regulatory landscape through ERMer. Furthermore, a Q&A module is included to showcase the power of graph databases in answering complex biological questions through simple queries. The backend graph model can be easily extended as new data become available. In addition, the framework implemented in ERMer can be easily migrated to other applications or organisms. ERMer is available at https://ermer.biodesign.ac.cn/.
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A, Hariprasad, and Murugan R. "Telegram Bot Using Cloud Services For Public Rescue Operations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 06, no. 04 (April 4, 2022). http://dx.doi.org/10.55041/ijsrem12156.
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Based on the study conducted on the Role of Social Media Applications in Public Rescue Operations during Disasters, identified many limitations in the previous applications only static information is been shared with the public, users cannot request for their needs and the application is not helping the rescue team for getting the information who needs help based on all this limitations and issues in the previous application This paper proposes a Telegram Linguistic Bot service using AWS Lex, Lambda, and RDS MySQL to bring both volunteers and victims together to ensure rescue operations are successful. By using this telegram bot, the victim and volunteer can enroll and start accessing the service. Similarly, the volunteers can also register with the application from anywhere, once victims send a help request for food and shelter the data will be stored in the cloud database which can be accessed by multiple rescue team management, so the team can easily identify which volunteers are nearby that particular victim and they can assign volunteer for fulfilling the victims needs this approach will help to reduce the difficulties in getting details of victims and volunteers. Using the victim’s data, the rescue team management will get a clarification on how many people needs helps for only food and shelters makes team have a proper plan. The proposed application is developed using a serverless framework which helps to reduce the cost of application usage also if any failure or any issue happened to the application, all the logs related to each action performed in the application will be stored in the cloud watch, using cloud watch we can easily keep track of applications if there is an issue which affects the performance of the application we can redeploy the application to another region this will ensure 99.9% availability of the application. Key Words: Telegram Bot, AWS, Public Rescue Community, Volunteers, Victims, Disaster.
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LaHaye, Stephanie, James R. Fitch, Kyle J. Voytovich, Adam C. Herman, Benjamin J. Kelly, Grant E. Lammi, Jeremy A. Arbesfeld, et al. "Discovery of clinically relevant fusions in pediatric cancer." BMC Genomics 22, no. 1 (December 2021). http://dx.doi.org/10.1186/s12864-021-08094-z.
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Abstract Background Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. Results Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information. Conclusions The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.