ORCID Number

0009-0000-4621-2015

Date of Award

Spring 4-2025

Embargo Period

4-1-2026

Access Type

Thesis - Open Access

Degree Name

Master of Science in Computer Science

Department

Electrical Engineering and Computer Science

Committee Chair

Laxima Niure Kandel

Committee Chair Email

Laxima.NiureKandel@erau.edu

First Committee Member

Omar Ochoa

First Committee Member Email

ochoao@erau.edu

Second Committee Member

David Bethelmy

Second Committee Member Email

David.Bethelmy@erau.edu

Third Committee Member

Shafika Showkat Moni

Third Committee Member Email

monis@erau.edu

College Dean

James W. Gregory

Abstract

Cloud computing has become a relatively new paradigm for the delivery of compute resources, with key management services (KMS) playing a crucial role in securely handling cryptographic operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, including SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryption/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction with KMS offerings from Ama- zon Web Services (AWS) and Microsoft Azure to conduct a comparative performance analysis. The methodology involves the AWS Cloud Development Kit (CDK) and the Bicep language to deploy AWS Lambda Functions and Azure Functions, respectively, to work with their respective KMS to conduct cryptographic workloads. Additionally, these workloads are executed on Elastic Compute Cloud (EC2) instances and Azure Virtual Machines using specific burst instance types. The performance assessment spans multiple configurations, including x86 64 and Arm64 architectures, various programming languages (Rust, Go, Python, Java, C#, and TypeScript), and function memory allocations. The findings highlight performance trade-offs between FaaS and IaaS compute paradigms for cryptographic workloads, emphasizing variations in execution speed and resource utilization. The impact of different hardware architectures, programming languages, memory configurations, and instance types is analyzed, providing information on optimal cloud deployment strategies for cryptographic workloads.

Download

Share

COinS