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

Evaluation of Serverless Computing Frameworks Based on Kubernetes

Recent advancements in virtualization and software architectures have led to the birth of the new paradigm of serverless computing. Serverless computing, also known as function-as-a-service, allows developers to deploy functions as computing units without worrying about the underlying infrastructure. Moreover, no resources are allocated or billed until a function is invoked. Thus, the major benefits of serverless computing are reduced developer concern about infrastructure, reduced time to market and lower cost. Currently, serverless computing is generally available through various public cloud service providers. However, there are certain bottlenecks on public cloud platforms, such as vendor lock-in, computation restrictions and regulatory restrictions. Thus, there is a growing interest to implement serverless computing on a private infrastructure. One of the preferred ways of implementing serverless computing is through the use of containers. A container-based solution allows to utilize features of existing orchestration frameworks, such as Kubernetes. This thesis discusses the implementation of serverless computing on Kubernetes. To this end, we carry out a feature evaluation of four open source serverless computing frameworks, namely Kubeless, OpenFaaS, Fission and OpenWhisk. Based on predefined criteria, we select Kubeless, Fission and OpenFaaS for further evaluation. First, we describe the developer experience on each framework. Next, we compare three different modes in which OpenFaaS functions are executed: HTTP, serializing and streaming. We evaluate the response time of function invocation and ease of monitoring and management of logs. We find that HTTP mode is the preferred mode for OpenFaaS. Finally, we evaluate the performance of the considered frameworks under different workloads. We find that Kubeless has the best performance among the three frameworks, both in terms of response time and the ratio of successful responses

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

Machine Learning Using Serverless Computing

Machine learning has been trending in the domain of computer science for quite some time. Newer and newer models and techniques are being developed every day. The adoption of cloud computing has only expedited the process of training machine learning. With its variety of services, cloud computing provides many options for training machine learning models. Leveraging these services is up to the user. Serverless computing is an important service offered by cloud service providers. It is useful for short tasks that are event-driven or periodic. Machine learning training can be divided into short tasks or batches to take advantage of this. Due to the nature of serverless computing, there are certain limitations imposed by the cloud service provider such as execution time and memory. This research proposes standalone solutions to overcome the challenges faced by serverless computing in training machine learning models. The research further combines these individual solutions and proposes a system for leveraging serverless computing for training a machine learning model that incorporates distributed machine learning