Rise of the Planet of Serverless Computing: A Systematic Review

Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications. It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164 papers on 17 research directions of serverless computing, including performance optimization, programming framework, application migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms for serverless computing, as well as promising research opportunities

QuickFaaS: providing portability and interoperability between FaaS Platforms

Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed during the code's execution time. One of the most popular serverless backend services is called Function-as-a-Service (FaaS), in which developers are often confronted with cloud-specific requirements. Function signature requirements, and the usage of custom libraries that are unique to cloud providers, were identified as the two main reasons for portability issues in FaaS applications, leading to various vendor lock-in problems. In this work, we define three cloud-agnostic models that compose FaaS platforms. Based on these models, we developed QuickFaaS, a multi-cloud interoperability desktop tool targeting cloud-agnostic functions and FaaS deployments. The proposed cloud-agnostic approach enables developers to reuse their serverless functions in different cloud providers with no need to change code or install extra software. We also provide an evaluation that validates the proposed solution by measuring the impact of a cloud-agnostic approach on the function's performance, when compared to a cloud-non-agnostic one. The study shows that a cloud-agnostic approach does not significantly impact the function's performance.info:eu-repo/semantics/publishedVersio

The Intersection of Function-as-a-Service and Stream Computing

With recent advancements in the field of computing including the emergence of cloud computing, the consumption and accessibility of computational resources have increased drastically. Although there have been significant movements towards more sustainable computing, there are many more steps to be taken to decrease the amount of energy consumed and greenhouse gases released from the computing sector. Historically, the switch from on-premises computing to cloud computing has led to less energy consumption through the design of efficient data centers. By releasing direct control of the hardware that their software is run on, an organization can also increase efficiency and reduce costs. A new development in cloud computing has been serverless computing. Even though the term "serverless" is a misnomer because all applications are still executed on servers, serverless lets an organization resign another level of control, managing instances of virtual machines, to their cloud provider in order to reduce their cost. The cloud provider then provisions resources on-demand enabling less idle time. This reduction of idle time is a direct reduction of computing resources used, therefore resulting in a decrease in energy consumption. One form of serverless computing, Function-as-a-Service (FaaS), may have a promising future replacing some stream computing applications in order to increase efficiency and reduce waste. To explore these possibilities, the development of a stream processing application using traditional methods through Kafka Streams and FaaS through AWS Lambda was completed in order to demonstrate that FaaS can be used for stateless stream processing

Reconocimiento de emociones de la voz aplicado sobre una arquitectura Cloud serverless

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2021-2022. The source code of this project can be found both in GitHub and Google Drive: https://github.com/RobertFarzan/Speech-Emotion-Recognition-system https://drive.google.com/file/d/1XobYLxcARE73EFwZ3VUr6Po7vum42ajh/view?usp=sharingThe purpose of this final degree thesis Applied speech emotion recognition on a serverless Cloud architecture is to do research into emotion recognition on human voice through several techniques including audio signal processing and deep learning technologies to classify a certain emotion detected on a piece of audio, as well as finding ways to deploy this functionality on Cloud (serverless). From there we can get a brief implementation of a streaming nearly real-time system in which an end user could record audio and retrieve responses of the emotions continuously. The idea intends to be a "emotion tracking system" that couples the technologies mentioned above along with a simple end-user GUI app that anyone could use purposefully to track their own voices in different situations - during a call, a meeting etc. - and get a brief summary visualization of their emotions across time with just a quick glance. This prototype seems to be one of the first software products of its kind, as there is a lot of literature on the Internet on Speech Emotion Recognition and tools for software engineers to facilitate this task but an easy final user product or solution for real-time SER appears to be non-existent. As a short summary of the project road map and the technologies involved, the process is as follows: development of a CNN model on Tensorflow 2.0 (with Python) to get emotion labels as output from a short chunk of audio as input; deployment of a Python script that uses this previously mentioned CNN model to return the emotion predictions in AWS Lambda (the Amazon service for serverless Cloud); and finally the design of a Python app with GUI integrated to send requests to the Lambda service and retrieve the responses with emotion predictions to present them with beautiful visualizations.El propósito de este TFG Reconocimiento de emociones de la voz aplicado sobre una arquitectura Clous serverless es investigar el reconocimiento de emociones en la voz humana usando diversas técnicas, entre las que se incluye el procesamiento de señal y deep learning para clasificar una cierta emoción en una pieza de audio, así como encontrar maneras de desplegar esta funcionalidad en el Cloud (serverless). A partir de estos pasos se podrá obtener una implementación de un sistema en streaming en tiempo cuasi real, en el que un usuario pueda grabarse a sí mismo y recibir respuestas cronológicas sobre su estado de ánimo continuamente. Esta idea trata de ser un "sistema monitor de emociones", que envuelva las tecnologías mencionadas arriba junto con una simple interfaz gráfica de usuario que cualquiera pueda usar para monitorizar intencionadamente su voz en diferentes situaciones - durante una llamada, una reunión etc. - y obtener una breve visualización de sus emociones a lo largo del tiempo en un simple vistazo. Este prototipo apunta a ser una de las primeras soluciones software de este tipo, ya que a pesar de haber mucha literatura en Internet acerca de Speech Emotion Recognition y herramientas para desarrolladores en esta tarea, parece no haber productos o soluciones de SER en tiempo real para usuarios. Como breve resumen de la hoja de ruta del proyecto y las tecnologías involucradas, el proceso es el siguiente: desarrollo de una red neuronal convolucional en TensorFlow 2.0 (con Python) para predecir emociones a partir de una pieza de audio como input; despliegue de un script de Python que use la red neuronal para devolver predicciones en AWS Lambda (el servicio de Amazon para serverless); y finalmente el diseño de una aplicación final para usuario en Python que incluya una interfaz gráfica que se conecte con los servicios de Lambda y devuelva respuestas con las predicciones y haga visualizaciones a partir de ellas.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Object as a Service (OaaS): Enabling Object Abstraction in Serverless Clouds

Function as a Service (FaaS) paradigm is becoming widespread and is envisioned as the next generation of cloud systems that mitigate the burden for programmers and cloud solution architects. However, the FaaS abstraction only makes the cloud resource management aspects transparent but does not deal with the application data aspects. As such, developers have to undergo the burden of managing the application data, often via separate cloud services (e.g., AWS S3). Similarly, the FaaS abstraction does not natively support function workflow, hence, the developers often have to work with workflow orchestration services (e.g., AWS Step Functions) to build workflows. Moreover, they have to explicitly navigate the data throughout the workflow. To overcome these problems of FaaS, we design a higher-level cloud programming abstraction that hides the complexities and mitigate the burden of developing cloud-native application development. We borrow the notion of object from object-oriented programming and propose a new abstraction level atop the function abstraction, known as Object as a Service (OaaS). OaaS encapsulates the application data and function into the object abstraction and relieves the developers from resource and data management burdens. It also unlocks opportunities for built-in optimization features, such as software reusability, data locality, and caching. OaaS natively supports dataflow programming such that developers define a workflow of functions transparently without getting involved in data navigation, synchronization, and parallelism aspects. We implemented a prototype of the OaaS platform and evaluated it under real-world settings against state-of-the-art platforms regarding the imposed overhead, scalability, and ease of use. The results demonstrate that OaaS streamlines cloud programming and offers scalability with an insignificant overhead to the underlying cloud system.Comment: This version of the paper has been significantly altered and the new observations have been obtained. Therefore, we withdraw the paper until the new version becomes availabl