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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Webb, Jeremiah"],"institution":null,"degree_name":"Master of Science in Computer Science","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-01T07:00:00Z","date_published":"2025-04-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Amazon Web Services","Azure","cloud computing","cryptography","function-as-a-service","infrastructure-as-code","infrastructure-as-a-service","microbenchmark","performance analysis","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Technology and Innovation"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/995","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Webb, Jeremiah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Amazon Web Services","Azure","cloud computing","cryptography","function-as-a-service","infrastructure-as-code","infrastructure-as-a-service","microbenchmark","performance analysis","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Technology and Innovation"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/995"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Comparative Performance Analysis of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study on FaaS and IaaS Platforms Dataset"]}]}],"canonical_facts":{"dc:creator":["Webb, Jeremiah"],"dc:date.available":["2026-04-01T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://commons.erau.edu/edt/995"],"dc:subject":["Amazon Web Services","Azure","cloud computing","cryptography","function-as-a-service","infrastructure-as-code","infrastructure-as-a-service","microbenchmark","performance analysis","Computer and Systems Architecture","Data Storage Systems","Digital Communications and Networking","Technology and Innovation"],"dc:title":["Comparative Performance Analysis of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study on FaaS and IaaS Platforms Dataset"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Computer Science"]},"updated_at":"2026-07-27T19:26:22Z"}