Estimating Cloud Application Performance Based on Micro-Benchmark Profiling

Scheuner, Joel; Leitner, Philipp

doi:10.1109/cloud.2018.00019

Cited by 28 publications

(24 citation statements)

References 32 publications

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“…Alam et al [24] 2018 Aral & Ovatman [27] 2018 Atrey et al [29] 2018 -Continued on next page- [34] 2018 Barrameda & Samaan [36] 2018 Borjigin et al [39] 2018 Bouet & Conan [40] 2018 Cheng et al [44] 2018 Diaz-Montes et al [53] 2018 Gill et al [60] 2018 Govindaraj & Artemenko [62] 2018 Guo & Shenoy [63] 2018 Guo et al [64] 2018 Guo et al [65] 2018 Hauser & Wesner [68] 2018 Heidari & Buyya [69] 2018 Jia et al [77] 2018 Jia et al [78] 2018 Khabbaz & Assi [83] 2018 Lahmann et al [88] 2018 Lin et al [93] 2018 Mikavica et al [109] 2018 Nawrocki & Sniezynski [117] 2018 Prakash et al [124] 2018 Prats et al [125] 2018 Rahimi et al [127] 2018 Sahni & Vidyarthi [131] 2018 Santos et al [133] 2018 Scheuner & Leitner [136] 2018 Simonis [139] 2018 Sofia & GaneshKumar [135] 2018 Stoyanov & Kollingbaum [141] 2018 Takahashi et al [142] 2018 Tesfatsion et al [144] 2018 Trihinas et al [145] 2018 Wang & Gelenbe [148] 2018 Wei et al [151] 2018 Xie & Jia [156] 2018 Yao & Ansari [160] 2018 Zhang & Wen [170] 2018 Zhang et al…”

Section: Cloud Resource Management: State Of the Artmentioning

confidence: 99%

“…Existing container orchestration tools exist for the deployment and management of containers, but these are still relatively young and still lack some important features that are offered in VM environments, for example for achieving high availability which includes live migration of running applications. [68] 2018 Prats et al [125] 2018 Scheuner & Leitner [136] 2018 Trihinas et al [145] 2018…”

Section: Highlights For Global Provisioning and Schedulingmentioning

confidence: 99%

“…Due to the immense growth in the cloud computing market and the resulting wide diversity of cloud services, micro-benchmarks could be used for identifying the best performing cloud services. As a result, Scheuner and Leitner have developed a cloud benchmarking methodology that uses micro-benchmarks to profile applications, in order to predict how an application performs on a wide range of cloud services [136]. The authors validated their approach using several metrics and micro-benchmarks with two applications from different domain.…”

Section: Application Demand Profilingmentioning

confidence: 99%

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Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

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Section: Cloud Resource Management: State Of the Artmentioning

confidence: 99%

Section: Highlights For Global Provisioning and Schedulingmentioning

confidence: 99%

Section: Application Demand Profilingmentioning

confidence: 99%

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Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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“…CWB was used to set up, in a fully automated and reproducible manner, web servers, databases, and a cluster of JMeter-based load generators. • In CLOUD'18 [18], CWB was utilized to collect microbenchmarking (CPU, IO, etc.) information about cloud instances, which was then used to estimate the application performance of different types of cloud applications or services.…”

Section: Example Studies Using Cwbmentioning

confidence: 99%

Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with Cloud WorkBench

Scheuner

Leitner

2019

Companion of the 2019 ACM/SPEC International Conference on Performance Engineering

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The continuing growth of the cloud computing market has led to an unprecedented diversity of cloud services with different performance characteristics. To support service selection, researchers and practitioners conduct cloud performance benchmarking by measuring and objectively comparing the performance of different providers and configurations (e.g., instance types in different data center regions). In this tutorial, we demonstrate how to write performance tests for IaaS clouds using the Web-based benchmarking tool Cloud WorkBench (CWB). We will motivate and introduce benchmarking of IaaS cloud in general, demonstrate the execution of a simple benchmark in a public cloud environment, summarize the CWB tool architecture, and interactively develop and deploy a more advanced benchmark together with the participants. CCS CONCEPTS• Computer systems organization → Cloud computing; • Software and its engineering → Software performance.

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“…Table 1 lists the micro benchmark tools and their version numbers used in this paper. For detailed reproduction description and benchmark configuration rationales, we refer to Scheuner [23]. 8 http://rspec.info/ 9 https://github.com/sealuzh/cwb-benchmarks/blob/master/rmit-combined/ 10 https://github.com/joe4dev/load-generator [29].…”

Section: Micromentioning

confidence: 99%

A Cloud Benchmark Suite Combining Micro and Applications Benchmarks

Scheuner

Leitner

2018

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering

Self Cite

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Micro and application performance benchmarks are commonly used to guide cloud service selection. However, they are often considered in isolation in a hardly reproducible setup with a flawed execution strategy. This paper presents a new execution methodology that combines micro and application benchmarks into a benchmark suite called RMIT Combined, integrates this suite into an automated cloud benchmarking environment, and implements a repeatable execution strategy. Additionally, a newly crafted Web serving benchmark called WPBench with three different load scenarios is contributed. A case study in the Amazon EC2 cloud demonstrates that choosing a cost-efficient instance type can deliver up to 40% better performance with 40% lower costs at the same time for the Web serving benchmark WPBench. Contrary to prior research, our findings reveal that network performance does not vary relevantly anymore. Our results also show that choosing a modern type of virtualization can improve disk utilization up to 10% for I/O-heavy workloads.

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Estimating Cloud Application Performance Based on Micro-Benchmark Profiling

Cited by 28 publications

References 32 publications

Resource Management in a Containerized Cloud: Status and Challenges

Resource Management in a Containerized Cloud: Status and Challenges

Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with Cloud WorkBench

A Cloud Benchmark Suite Combining Micro and Applications Benchmarks

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