Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open source projects, identify key characteristics and use cases, and describe technical challenges and open problems.

The server is dead, long live the server. BY PAUL CASTRO, VATCHE ISHAKIAN, VINOD MUTHUSAMY, AND ALEKSANDER SLOMINSKI key insights ˽ Serverless computing takes the original promises of cloud computing and delivers true pay only for resources used with almost infinite scalability while hiding the details of how servers are used and maintained. ˽ Serverless computing is a new cloud computing paradigm with enormous economic growth potential. ˽ Serverless computing allows the developer to focus on developing business logic and gives the cloud provider additional control over optimizing resources. ˽ There are many technical challenges and opportunities for research.

Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.

Distributed software component architectures provide a promising approach to the problem of building large scale, scientific Grid applications [18]. Communication in these component architectures is based on Remote Method Invocation (RMI) protocols that allow one software component to invoke the functionality of another. Examples include Java remote method invocation (Java RMI)[25] and the new Simple Object Access Protocol (SOAP) [15]. SOAP has the advantage that many programming languages and component frameworks can support it. This paper describes experiments showing that SOAP by itself is not efficient enough for large scale scientific applications. However, when it is embedded in a multi-protocol RMI framework, SOAP can be effectively used as a universal control protocol, that can be swapped out by faster, more special purpose protocols when large data transfer speeds are needed.

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