Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Navrati Saxena

Third Advisor

Thomas Austin

Keywords

Serverless, AWS, Lambda, S3, EFS, EC2, VM, GCF

Abstract

Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to optimize the cloud-deployment architecture to obtain accurate results efficiently. In addition, serverless computing suffers from drawbacks like cold start etc., which further increases the need of researching different serverless provisioning tools and techniques. This research work aims to deploy a machine learning model to detect real-time crisis, using various serverless computing resources provided by notable cloud vendors like Amazon Web Services (AWS) and Google Cloud Platform (GCP). It also compares among the various methodologies available and later aims to build a training platform for machine learning tasks.

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