627 research outputs found
A review on serverless architectures - function as a service (FaaS) in cloud computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented
Adapting Microservices in the Cloud with FaaS
This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework
When Edge Meets FaaS: Opportunities and Challenges
The proliferation of edge devices and the rapid growth of IoT data have
called forth the edge computing paradigm. Function-as-a-service (FaaS) is a
promising computing paradigm to realize edge computing. This paper explores the
feasibility and advantages of FaaS-based edge computing. It also studies the
research challenges that should be addressed in the design of such systems,
which are 1) the quick decomposing and recomposing of applications, 2) the
trade-off between performance and isolation of sandbox mechanisms, and 3)
distributed scheduling. The challenges are illustrated by evaluating existing
FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS
Towards sustainable ecosystems for cloud functions
The main technologies around modern cloud development and deployment paradigms such as Function-as-a-Service (FaaS) environments follow a typical technology life-cycle. Starting with basic code installation and execution environments, they unfold into a complete ecosystem with rich collaborative development and market enablement tools. In this paper, we analyse the growth of such ecosystems, reveal causes of hindrances in previous service-oriented approaches, and present a vision of how an ecosystem with sustainable operation could look like both in general and specifically for cloud functions. We present Function Hub, a partial prototypical implementation to gain first knowledge about the potential operational ecosystem behaviour
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within
low latency Function as a Service (FaaS) prediction pipelines: including image
recognition, object detection, natural language processing, speech synthesis,
and personalized recommendation pipelines. Cloud computing, as the de-facto
backbone of modern computing infrastructure for both enterprise and consumer
applications, has to be able to handle user-defined pipelines of diverse DNN
inference workloads while maintaining isolation and latency guarantees, and
minimizing resource waste. The current solution for guaranteeing isolation
within FaaS is suboptimal -- suffering from "cold start" latency. A major cause
of such inefficiency is the need to move large amount of model data within and
across servers. We propose TrIMS as a novel solution to address these issues.
Our proposed solution consists of a persistent model store across the GPU, CPU,
local storage, and cloud storage hierarchy, an efficient resource management
layer that provides isolation, and a succinct set of application APIs and
container technologies for easy and transparent integration with FaaS, Deep
Learning (DL) frameworks, and user code. We demonstrate our solution by
interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x
speedup in latency for image classification models and up to 210x speedup for
large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201
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