Performance Measurement of a Private Cloud in the OpenCirrusTM Testbed

Baun, Christian; Kunze, M.

doi:10.1007/978-3-642-14122-5_49

Cited by 12 publications

(14 citation statements)

References 2 publications

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“…Private benchmark/ application [6] Compiling Linux Kernel [46] Fibonacci [12] DGEMM [17] HPL [17] NPB [41] Other CPU Load (%) SPECweb 2005 [22] Ubench CPU Score Ubench [47] 3) Memory (Cache) Evaluation Metrics (cf . Table III): Memory (Cache) is intended for fast access to temporarily saved data that can be achieved from slow-accessed hard drive storage.…”

Section: ) Communication Evaluation Metrics (Cf Table I)mentioning

confidence: 99%

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

O’Brien

et al. 2012

2012 ACM/IEEE 13th International Conference on Grid Computing

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Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23, 201

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Section: ) Communication Evaluation Metrics (Cf Table I)mentioning

confidence: 99%

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

O’Brien

et al. 2012

2012 ACM/IEEE 13th International Conference on Grid Computing

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“…Furthermore, by taking a particular Cloud resource being evaluated as a reference, the factor activity direction can be depicted as input or output [2]. As for the activity sequence in a workload, the arrangement generates either sequential [2] or parallel [14] activity flows.…”

Section: A Workload Factorsmentioning

confidence: 99%

“…2) Computation: When evaluating PaaS, the Computation resource is usually regarded as a black box [16]. Whereas, for IaaS, the practices of Computation evaluation of Cloud services have taken into account Core Number [3], Elastic Compute Unit (ECU) Number, Thread Number [2], and a set of CPU characteristics. Note that, compared to physical CPU core and thread, ECU is a logical concept introduced by Amazon, which is defined as the CPU power of a 1.0-1.2 GHz 2007 Opteron or Xeon processor [28].…”

Section: B Computing Resource Factorsmentioning

confidence: 99%

A factor framework for experimental design for performance evaluation of commercial cloud services

O’Brien

Zhang

et al. 2012

4th IEEE International Conference on Cloud Computing Technology and Science Proceedings

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Given the diversity of commercial Cloud services, performance evaluations of candidate services would be crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of service improvement). Before an evaluation implementation, the selection of suitable factors (also called parameters or variables) plays a prerequisite role in designing evaluation experiments. However, there seems a lack of systematic approaches to factor selection for Cloud services performance evaluation. In other words, evaluators randomly and intuitively concerned experimental factors in most of the existing evaluation studies. Based on our previous taxonomy and modeling work, this paper proposes a factor framework for experimental design for performance evaluation of commercial Cloud services. This framework capsules the state-of-the-practice of performance evaluation factors that people currently take into account in the Cloud Computing domain, and in turn can help facilitate designing new experiments for evaluating Cloud services.

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“…Analysis of gene expression data and microarrays is being pursued in public clouds [13]. Other studies are beginning to appear that address the performance of science applications in the cloud [14] [15]. While these efforts and others are pioneering the use of science applications in the cloud, we believe that there are many unresolved issues on how, in particular, domain scientists can best leverage and exploit cloud computing.…”

Section: Existing Use Of Cloud Computing For Esciencementioning

confidence: 99%

eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform

Humphrey

Agarwal

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing (IPDPS)

106

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The combination of low-cost sensors, low-cost commodity computing, and the Internet is enabling a new era of data-intensive science. The dramatic increase in this data availability has created a new challenge for scientists: how to process the data. Scientists today are envisioning scientific computations on large scale data but are having difficulty designing software architectures to accommodate the large volume of the often heterogeneous and inconsistent data. In this paper, we introduce a particular instance of this challenge, and present our design and implementation of a MODIS satellite data reprojection and reduction pipeline in the Windows Azure cloud computing platform. This cloud-based pipeline is designed with a goal of hiding data complexities and subsequent data processing and transformation from end users. This pipeline is highly flexible and extensible to accommodate different science data processing tasks, and can be dynamically scaled to fulfill scientists' various computational requirements in a cost-efficient way. Experiments show that by running a practical large-scale science data processing job in the pipeline using 150 moderately-sized Azure virtual machine instances, we were able to produce analytical results in nearly 90X less time than was possible with a high-end desktop machine. To our knowledge, this is one of the first eScience applications to use the Windows Azure platform.

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Performance Measurement of a Private Cloud in the OpenCirrusTM Testbed

Cited by 12 publications

References 2 publications

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

A factor framework for experimental design for performance evaluation of commercial cloud services

eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform

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