Multidimensional cloud latency monitoring and evaluation

Tomanek, Ondrej; Mulinka, Pavol; Kencl, Lukáš

doi:10.1016/j.comnet.2016.06.011

Cited by 18 publications

(5 citation statements)

References 15 publications

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“…The IEEE Intercloud Testbed project [26] has been created to facilitate the development of Intercloud. Both multicloud and Intercloud approaches could take advantage of tools such as Cloud Crawler [27] and CLAudit [28] that enable cloud performance monitoring over time.…”

Section: Discussionmentioning

confidence: 99%

Enabling Cloud-Based Computational Fluid Dynamics With a Platform-as-a-Service Solution

Taylor

Anagnostou

Kiss

et al. 2019

IEEE Trans. Ind. Inf.

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Computational Fluid Dynamics (CFD) is widely used in manufacturing and engineering from product design to testing. CFD requires intensive computational power and typically needs high performance computing to reduce potentially long experimentation times. Dedicated high performance computing systems are often expensive for small-to-medium enterprises (SMEs). Cloud computing claims to enable low cost access to high performance computing without the need for capital investment. The CloudSME Simulation Platform aims to provide a flexible and easy to use cloud-based Platform-as-a-Service (PaaS) technology that can enable SMEs to realize the benefits of high performance computing. Our Platform incorporates workflow management and multi-cloud implementation across various cloud resources. Here we present the components of our technology and experiences in using it to create a cloudbased version of the TransAT CFD software. Three case studies favourably compare the performance of a local cluster and two different clouds and demonstrate the viability of our cloud-based approach. Index Terms-Computational fluid dynamics, cloud computing, modeling and simulation, high performance computing, fluid mechanics.

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Section: Discussionmentioning

confidence: 99%

Enabling Cloud-Based Computational Fluid Dynamics With a Platform-as-a-Service Solution

Taylor

Anagnostou

Kiss

et al. 2019

IEEE Trans. Ind. Inf.

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“…However, CSPs only reveal insufficient amount of technical information about their service, leaving tenants indecisive. But, in fact, there are big differences in service quality among CSPs and even among single-CSP's datacenters (DCs), as was shown previously [17,18].…”

Section: Introductionmentioning

confidence: 91%

Latency-based benchmarking of cloud service providers

Uhlir

Tomanek

Kencl

2016

Proceedings of the 9th International Conference on Utility and Cloud Computing

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With the ever-increasing trend of migration of applications to the Cloud environment, there is a growing need to thoroughly evaluate quality of the Cloud service itself, before deciding upon a hosting provider. Benchmarking the Cloud services is difficult though, due to the complex nature of the Cloud Computing setup and the diversity of locations, of applications and of their specific service requirements. However, such comparison may be crucial for decision making and for troubleshooting of services offered by the intermediate businesses-the so-called Cloud tenants. Existing crosssectional studies and benchmarking methodologies provide only a shallow comparison of Cloud services, whereas stateof-the-art tooling for specific comparisons of applicationperformance parameters, such as for example latency, is insufficient. In this work, we propose a novel methodology for benchmarking of Cloud-service providers, which is based on latency measurements collected via active probing, and can be tailored to specific application needs. Furthermore, we demonstrate its applicability on a practical longitudinal study of real measurements of two major Cloud-service providers-Amazon and Microsoft.

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“…Finally, there is the cloud layer, which houses the remote computing resources. To reach this layer data must travel through the internet, for which we assume a communication time of 1 ns per bit, based on round trip times to AWS EC2 instances measured in [43]. These communication times are estimates and ignore frame/packet overheads, and many other delays, but are there to model variation in transfer time between different layers.…”

Section: Networkmentioning

confidence: 99%