The mOSAIC Benchmarking Framework: Development and Execution of Custom Cloud Benchmarks

Aversano, Giuseppe; Rak, Massimiliano; Villano, Umberto

doi:10.12694/scpe.v14i1.825

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…A benchmarking framework was developed in the frame of the mOSAIC project (a detailed description of the framework can be found in Aversano, Rak & Villano ). It includes a set of special components to be included in an application in order to measure its performance.…”

Section: Resultsmentioning

confidence: 99%

Cloud resource orchestration within an open‐source component‐based platform as a service

Petcu

Panica

Crăciun

et al. 2013

Concurrency and Computation

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SUMMARYIn the growing market of cloud computing dominated by proprietary solutions, the adoption of open-source and deployable middleware that hides the service heterogeneity and ensure code portability can provide important benefits for the fast development of new services. Therefore, this paper exposes the mechanisms for orchestrating cloud-enabled hardware and software resources supported by a recently developed open-source platform as a service.

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Section: Resultsmentioning

confidence: 99%

Cloud resource orchestration within an open‐source component‐based platform as a service

Petcu

Panica

Crăciun

et al. 2013

Concurrency and Computation

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“…The choice between hosting options can be made by evaluating the balance between performance and cost for various VM sizes and providers. Work targeting providers benchmark includes MOSAIC [20,21], CLOUDCRAWLER [22] framework, and C-METER [23] where the monitored application can be configured. Other approaches compare application metrics on provided benchmarks [6,17,24].…”

Section: B Cloud System Under Testmentioning

confidence: 99%

CloudFactory: An Open Toolkit to Generate Production-like Workloads for Cloud Infrastructures

Jacquet,

Ledoux,

Rouvoy

2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

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Cloud infrastructures are large-scale and complex platforms designed to host a wide diversity of applications and workloads. Given these complexity and scale factors, simulators and benchmarks are broadly adopted in vitro to study their behaviors, prototype new software components and heuristics, and evaluate their effective performances.However, both state-of-the-art simulations and benchmarks may suffer from a representativeness problem, as the reported results can vary depending on their input workloads. For example, a Infrastructure-as-a-Service (IAAS) platform aims to host Virtual Machines (VMs), whose characteristics (resource configurations, workload intensity, arrival/departure rate, etc.) can greatly differ depending on Cloud providers and public/private deployments. Addressing this IAAS representativeness thus requires Cloud providers to share production-scale datasets, which might be considered sensitive. Moreover, Simulations and benchmarks require a specific experiment scenario that cannot be easily generated from Cloud providers characteristics.To address these issues, this paper introduces CLOUDFACTORY, a IAAS workload generator. Our contribution is first composed of a library that can be used by Cloud providers to share IAAS statistics, instead of raw datasets. Then, we introduce a generator designed to produce realistic VM workloads that match these statistics. CLOUDFACTORY is made available as open-source software that can be adopted by Cloud providers and researchers to foster the evaluation of new contributions.As an example, we perform an analysis on scheduling evolution for different IAAS workload intensity of two different Cloud providers: Microsoft Azure and Chameleon. We also report on OVHcloud statistics computed from CLOUDFACTORY and compare them to other Cloud providers.

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“…In order to estimate the time behavior of the above mentioned components we use an approach based on the execution of ad-hoc benchmarks, which run using the benchmarking framework integrated in mOSAIC API [8]. The methodology used is thoroughly dealt with in paper [14].…”

Section: Tuning Of Algorithm-dependent Componentsmentioning

confidence: 99%

Early Prediction of the Cost of Cloud Usage for HPC Applications

Rak¹,

Turtur²,

Villano³

2015

SCPE

Self Cite

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After a decade of diffusion, cloud computing has received wide acceptance, but it is not yet attractive for the HPC community. Clouds could be a cost-effective alternative to clusters and supercomputers, providing economy of scale, elasticity, flexibility, and easy customization. Unfortunately, most clouds are optimized for running business applications, not for HPC. However, they can be profitably used to run small-scale parallelism codes. This paper presents a framework built on the top of a cloud-aware programming platform (mOSAIC) for the development of bag-of-tasks scientific applications. The framework integrates a cloud-based simulation environment able to predict the behavior of the developed applications. Simulations enable the developer to predict at an early development stage performance and cloud resource usage, and so the infrastructure lease cost on a public cloud. The paper sketches the framework organization and presents the approach followed for the performance simulation of applications, focusing on a software development methodology that hinges on early performance prediction. After showing the results of some validation tests of simulation accuracy, an example of early performance prediction is presented.

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The mOSAIC Benchmarking Framework: Development and Execution of Custom Cloud Benchmarks

Cited by 7 publications

References 15 publications

Cloud resource orchestration within an open‐source component‐based platform as a service

Cloud resource orchestration within an open‐source component‐based platform as a service

CloudFactory: An Open Toolkit to Generate Production-like Workloads for Cloud Infrastructures

Early Prediction of the Cost of Cloud Usage for HPC Applications

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