Serverless Nanopore Basecalling with AWS Lambda

2021 ◽  
pp. 578-586
Author(s):  
Piotr Grzesik ◽  
Dariusz Mrozek
Keyword(s):  
Aws Lambda

scholarly journals Geospatial Serverless Computing: Architectures, Tools and Future Directions

2020 ◽  
Vol 9 (5) ◽  
pp. 311 ◽  
Author(s):  
Sujit Bebortta ◽  
Saneev Kumar Das ◽  
Meenakshi Kandpal ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Big Data ◽  
Forest Cover ◽  
Mineral Resources ◽  
Hyperspectral Data ◽  
Real Time Analysis ◽  
First Case ◽  
Amazon Web Services ◽  
Aws Lambda ◽  
Physical Features

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.

scholarly journals GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

2021 ◽  
Vol 11 (4) ◽  
pp. 1438
Author(s):  
Sebastián Risco ◽  
Germán Moltó
Keyword(s):  
Video Processing ◽  
Cost Effective ◽  
Multimedia Processing ◽  
Computing Services ◽  
Video Frames ◽  
Open Source Framework ◽  
Cloud Infrastructures ◽  
Aws Lambda ◽  
Runtime Environments

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.

scholarly journals Comparative study of cloud platforms to develop a Chatbot

2017 ◽  
Vol 6 (3) ◽  
pp. 57 ◽  
Author(s):  
Amit Patil ◽  
Marimuthu K ◽  
Nagaraja Rao A ◽  
Niranchana R
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Time Complexity ◽  
Completion Time ◽  
Setup Time ◽  
Human Being ◽  
Cloud Platform ◽  
New Era ◽  
Ibm Watson ◽  
Aws Lambda

Before chatbots there were simply bots: The invention of a chatbot brought us to the new era of technology, the era of conversation service. A chatbot is a virtual person that can effectively talk to any human being with the help of interactive conversion textual skill. Now a days there are many cloud-based platforms available for developing and deploying the chatbot such as Microsoft bot framework, IBM Watson, Kore, AWS lambda, Microsoft Azure bot service, Chatfuel, Heroku and many more but all those techniques has some drawbacks such as built-in Artificial Intelligence, NLP, conversion service, programming etc. This paper represents the comparison between all cloud-based chatbot technologies with some constraint such as built-in AI, setup time, completion time, complexity etc. Finally, by the comparison, we will get to know that which cloud platform is efficient and suitable for developing chatbot.

Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions

2020 ◽  
Vol 110 ◽  
pp. 502-514 ◽  
Author(s):  
Maciej Malawski ◽  
Adam Gajek ◽  
Adam Zima ◽  
Bartosz Balis ◽  
Kamil Figiela
Keyword(s):  
Scientific Workflows ◽  
Aws Lambda

Distributed Parallel Analysis Engine for High Energy Physics Using AWS Lambda

2020 ◽  
Author(s):  
Jacek Kuśnierz ◽  
Maciej Malawski ◽  
Vincenzo Eduardo Padulano ◽  
Enric Tejedor Saavedra ◽  
Pedro Alonso-Jorda
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Parallel Analysis ◽  
Aws Lambda ◽  
Analysis Engine ◽  
Energy Physics

Case Study: Use of AWS Lambda for Building a Serverless Chat Application

2020 ◽  
pp. 237-244
Author(s):  
Brijesh Choudhary ◽  
Chinmay Pophale ◽  
Aditya Gutte ◽  
Ankit Dani ◽  
S. S. Sonawani
Keyword(s):  
Aws Lambda

scholarly journals A Traffic Analysis on Serverless Computing Based on the Example of a File Upload Stream on AWS Lambda

2020 ◽  
Vol 4 (4) ◽  
pp. 38
Author(s):  
Lisa Muller ◽  
Christos Chrysoulas ◽  
Nikolaos Pitropakis ◽  
Peter J. Barclay
Keyword(s):  
Cold Start ◽  
Future Research ◽  
Cloud Provider ◽  
Trip Time ◽  
Effective Delivery ◽  
Recent Developments ◽  
File Upload ◽  
Aws Lambda ◽  
Functional Components ◽  
Incoming Request

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.

scholarly journals Scheduling Hardware-Accelerated Cloud Functions

2021 ◽  
Author(s):  
Jessica Vandebon ◽  
Jose G. F. Coutinho ◽  
Wayne Luk
Keyword(s):  
Resource Selection ◽  
Single Type ◽  
Resource Configuration ◽  
Domain Specific ◽  
Heterogeneous Scheduling ◽  
Different Types ◽  
Improved Performance ◽  
Heterogeneous Cloud ◽  
Cloud Infrastructures ◽  
Aws Lambda

AbstractThis paper presents a Function-as-a-Service (FaaS) approach for deploying managed cloud functions onto heterogeneous cloud infrastructures. Current FaaS systems, such as AWS Lambda, allow domain-specific functionality, such as AI, HPC and image processing, to be deployed in the cloud while abstracting users from infrastructure and platform concerns. Existing approaches, however, use a single type of resource configuration to execute all function requests. In this paper, we present a novel FaaS approach that allows cloud functions to be effectively executed across heterogeneous compute resources, including hardware accelerators such as GPUs and FPGAs. We implement heterogeneous scheduling to tailor resource selection to each request, taking into account performance and cost concerns. In this way, our approach makes use of different processor types and quantities (e.g. 2 CPU cores), uniquely suited to handle different types of workload, potentially providing improved performance at a reduced cost. We validate our approach in three application domains: machine learning, bio-informatics, and physics, and target a hardware platform with a combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to traditional FaaS, our approach achieves a cost improvement for non-uniform traffic of up to 8.9 times, while maintaining performance objectives.

scholarly journals A mixed-method empirical study of Function-as-a-Service software development in industrial practice

2018 ◽  
Author(s):  
Philipp Leitner ◽  
Erik Wittern ◽  
Josef Spillner ◽  
Waldemar Hummer
Keyword(s):  
Mixed Method ◽  
Grey Literature ◽  
Systematic Research ◽  
Systematic Analysis ◽  
Web Based ◽  
Computing Services ◽  
Architectural Styles ◽  
Cloud Computing Services ◽  
Aws Lambda ◽  
Application Developers

Function-as-a-Service (FaaS) describes cloud computing services that make infrastructure components transparent to application developers, thus falling in the larger group of “serverless” computing models. When using FaaS offerings, such as AWS Lambda, developers provide atomic and short-running code for their functions, and FaaS providers execute and horizontally scale them on- demand . Currently, there is no systematic research on how developers use serverless, what types of applications lend themselves to this model, or what architectural styles and practices FaaS-based applications are based on. We present results from a mixed-method study, combining interviews with advanced practitioners, a systematic analysis of grey literature, and a Web-based survey. We find that successfully adopting FaaS requires a different mental model, where systems are primarily constructed by composing pre-existing services, with FaaS often acting as the “glue” that brings these services together. Tooling availability and maturity, especially related to testing and deployment, remains a major difficulty. Further, we find that current FaaS systems lack systematic support for function reuse, and abstractions and programming models for building non-trivial FaaS applications are limited . We conclude with a discussion of implications for FaaS providers, software developers, and researchers.

Sign in / Sign up

Export Citation Format

Share Document