Towards a Security-Aware Benchmarking Framework for Function-as-a-Service
May 15, 2019 Β· Declared Dead Β· π International Conference on Cloud Computing and Services Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Roland Pellegrini, Igor Ivkic, Markus Tauber
arXiv ID
1905.07228
Category
cs.SE: Software Engineering
Cross-listed
cs.CR
Citations
5
Venue
International Conference on Cloud Computing and Services Science
Last Checked
4 months ago
Abstract
In a world, where complexity increases on a daily basis the Function-as-a-Service (FaaS) cloud model seams to take countermeasures. In comparison to other cloud models, the fast evolving FaaS increasingly abstracts the underlying infrastructure and refocuses on the application logic. This trend brings huge benefits in application and performance but comes with difficulties for benchmarking cloud applications. In this position paper, we present an initial investigation of benchmarking FaaS in close to reality production systems. Furthermore, we outline the architectural design including the necessary benchmarking metrics. We also discuss the possibility of using the proposed framework for identifying security vulnerabilities.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted