Cloud Benchmarking for Performance

Varghese, Blesson; Akgün, Özgür; Miguel, Ian; Thai, Long; Barker, Adam

doi:10.1109/cloudcom.2014.28

Cited by 22 publications

(31 citation statements)

References 10 publications

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“…Benchmarking is a de facto approach for capturing the performance (of entities such as memory, CPU, storage, network, etc) of a computing system [157]. Metrics relevant to the performance of each entity need to be captured using standard performance evaluation tools.…”

Section: B Benchmarkingmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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Section: B Benchmarkingmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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“…Others use profilebased methods (cf. [2], [11], [23]) or application-specific performance models (cf. [25]).…”

Section: Related Workmentioning

confidence: 99%

Same Same, but Different: A Descriptive Intra-IaaS Differentiation

Elkhatib

Samreen

Blair

2019

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Users of cloud computing are overwhelmed with choice, even within the services offered by one provider. As such, many users select cloud services based on description alone. We investigate the degree to which such strategy is optimal. In this quantitative study, we investigate the services of 2 of major IaaS providers. We use 2 representative applications to obtain longitudinal observations over 7 days of the week and over different times of the day, totalling over 14,000 executions. We give evidence of significant variations of performance offered within IaaS services, calling for brokers to use automated and adaptive decision making processes with means for incorporating expressive user constraints.

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“…Benchmark data is then used to help rank the performance of VMs [3], [4]. However, cloud benchmarking methods are typically heavyweight -by this we mean time consuming processes to benchmark an entire VM, which incur significant monetary costs.…”

Section: Introductionmentioning

confidence: 99%

“…However, cloud benchmarking methods are typically heavyweight -by this we mean time consuming processes to benchmark an entire VM, which incur significant monetary costs. Heavyweight benchmarks [3] are usually obtained by running the benchmarking tool on the entire VM. For example, the cr1.8xlarge VM with 244GiB RAM will require over 10 hours to be benchmarked.…”

Section: Introductionmentioning

confidence: 99%

Container-Based Cloud Virtual Machine Benchmarking

Varghese

Subba

Thai

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

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With the availability of a wide range of cloud Virtual Machines (VMs) it is difficult to determine which VMs can maximise the performance of an application. Benchmarking is commonly used to this end for capturing the performance of VMs. Most cloud benchmarking techniques are typically heavyweight -time consuming processes which have to benchmark the entire VM in order to obtain accurate benchmark data. Such benchmarks cannot be used in real-time on the cloud and incur extra costs even before an application is deployed.In this paper, we present lightweight cloud benchmarking techniques that execute quickly and can be used in near realtime on the cloud. The exploration of lightweight benchmarking techniques are facilitated by the development of DocLite -Docker Container-based Lightweight Benchmarking. DocLite is built on the Docker container technology which allows a user-defined portion (such as memory size and the number of CPU cores) of the VM to be benchmarked. DocLite operates in two modes, in the first mode, containers are used to benchmark a small portion of the VM to generate performance ranks. In the second mode, historic benchmark data is used along with the first mode as a hybrid to generate VM ranks. The generated ranks are evaluated against three scientific high-performance computing applications. The proposed techniques are up to 91 times faster than a heavyweight technique which benchmarks the entire VM. It is observed that the first mode can generate ranks with over 90% and 86% accuracy for sequential and parallel execution of an application. The hybrid mode improves the correlation slightly but the first mode is sufficient for benchmarking cloud VMs.

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Cloud Benchmarking for Performance

Cited by 22 publications

References 10 publications

Resource Management in Fog/Edge Computing

Resource Management in Fog/Edge Computing

Same Same, but Different: A Descriptive Intra-IaaS Differentiation

Container-Based Cloud Virtual Machine Benchmarking

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