Szczegóły publikacji
Opis bibliograficzny
Performance evaluation of heterogeneous cloud functions / Kamil FIGIELA, Adam GAJEK, Adam Zima, Beata Obrok, Maciej MALAWSKI // Concurrency and Computation : Practice and Experience ; ISSN 1532-0626. — 2018 — vol. 30 iss. 23 spec. iss. art. no. e4792, s. 1–16. — Bibliogr. s. 15–16, Summ. — Publikacja dostępna online od: 2018-08-24. — HCW : the twenty sixth international Heterogeneity in Computing Worhshop : [May 29, 2017 : Orlando] ; HeteroPar : fifteenth international workshop on algorithms, models and tools for parallel omputing on Heterogeneous Platforms : [August 28th, 2017, Santiago d Compostella] ; HPDeepL2017 : High Performance Deep learning techniques for big data analytics
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 119565 |
|---|---|
| Data dodania do BaDAP | 2019-03-19 |
| Tekst źródłowy | URL |
| DOI | 10.1002/cpe.4792 |
| Rok publikacji | 2018 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Concurrency and Computation : Practice & Experience |
Abstract
Cloud Functions, often called Function-as-a-Service (FaaS), pioneered by AWS Lambda, are an increasingly popular method of running distributed applications. As in other cloud offerings, cloud functions are heterogeneous due to variations in underlying hardware, runtime systems, as well as resource management and billing models. In this paper, we focus on performance evaluation of cloud functions, taking into account heterogeneity aspects. We developed a cloud function benchmarking framework, consisting of one suite based on Serverless Framework and one based on HyperFlow. We deployed the CPU-intensive benchmarks: Mersenne Twister and Linpack. We measured the data transfer times between cloud functions and storage, and we measured the lifetime of the runtime environment. We evaluated all the major cloud function providers: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. We made our results available online and continuously updated. We report on the results of the performance evaluation, and we discuss the discovered insights into resource allocation policies.
