422 research outputs found
Serverless Computing for Scientific Applications
Serverless computing has become an important model in cloud computing and
influenced the design of many applications. Here, we provide our perspective on
how the recent landscape of serverless computing for scientific applications
looks like. We discuss the advantages and problems with serverless computing
for scientific applications, and based on the analysis of existing solutions
and approaches, we propose a science-oriented architecture for a serverless
computing framework that is based on the existing designs. Finally, we provide
an outlook of current trends and future directions
A Novel Approach for Triggering the Serverless Function in Serverless Environment
Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend
BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration
Machine learning (ML) continues to permeate all layers of academia, industry
and society. Despite its successes, mental frameworks to capture and represent
machine learning workflows in a consistent and coherent manner are lacking. For
instance, the de facto process modeling standard, Business Process Model and
Notation (BPMN), managed by the Object Management Group, is widely accepted and
applied. However, it is short of specific support to represent machine learning
workflows. Further, the number of heterogeneous tools for deployment of machine
learning solutions can easily overwhelm practitioners. Research is needed to
align the process from modeling to deploying ML workflows.
We analyze requirements for standard based conceptual modeling for machine
learning workflows and their serverless deployment. Confronting the
shortcomings with respect to consistent and coherent modeling of ML workflows
in a technology independent and interoperable manner, we extend BPMN's
Meta-Object Facility (MOF) metamodel and the corresponding notation and
introduce BPMN4sML (BPMN for serverless machine learning). Our extension
BPMN4sML follows the same outline referenced by the Object Management Group
(OMG) for BPMN. We further address the heterogeneity in deployment by proposing
a conceptual mapping to convert BPMN4sML models to corresponding deployment
models using TOSCA.
BPMN4sML allows technology-independent and interoperable modeling of machine
learning workflows of various granularity and complexity across the entire
machine learning lifecycle. It aids in arriving at a shared and standardized
language to communicate ML solutions. Moreover, it takes the first steps toward
enabling conversion of ML workflow model diagrams to corresponding deployment
models for serverless deployment via TOSCA.Comment: 105 pages 3 tables 33 figure
RADON: Rational decomposition and orchestration for serverless computing
Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models
Function-as-a-Service Performance Evaluation: A Multivocal Literature Review
Function-as-a-Service (FaaS) is one form of the serverless cloud computing
paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing
event-triggered code snippets (i.e., functions). Many studies that empirically
evaluate the performance of such FaaS platforms have started to appear but we
are currently lacking a comprehensive understanding of the overall domain. To
address this gap, we conducted a multivocal literature review (MLR) covering
112 studies from academic (51) and grey (61) literature. We find that existing
work mainly studies the AWS Lambda platform and focuses on micro-benchmarks
using simple functions to measure CPU speed and FaaS platform overhead (i.e.,
container cold starts). Further, we discover a mismatch between academic and
industrial sources on tested platform configurations, find that function
triggers remain insufficiently studied, and identify HTTP API gateways and
cloud storages as the most used external service integrations. Following
existing guidelines on experimentation in cloud systems, we discover many flaws
threatening the reproducibility of experiments presented in the surveyed
studies. We conclude with a discussion of gaps in literature and highlight
methodological suggestions that may serve to improve future FaaS performance
evaluation studies.Comment: improvements including postprint update
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