Metabolic Flux Analysis in the Cloud

Dalman, Tolga; Doernemann, Tim; Juhnke, Ernst; Weitzel, Michael; Smith, Matthew; Wiechert, Wolfgang; Nöh, Katharina; Freisleben, Bernd

doi:10.1109/escience.2010.20

Cited by 10 publications

(13 citation statements)

References 14 publications

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“…In fact, unlike evaluating other performance properties, the evaluation of Scalability (and also Variability) normally implies comparison among a set of data that can be conveniently organized in charts and tables. FEFF84 MPI [48] MapReduce App [47] MCB Hadoop [50] MG-RAST+BLAST [37] MODIS Processing [15] NPB-OMP/MPI [51] WCD [23] WSTest [49] Benchmark Transactional Job Delay (min, s)…”

Section: ) Scalability Evaluation Metrics (Cf Table Vi)mentioning

confidence: 99%

“…HPL [17] Lublin99 [33] MCB Hadoop [50] Montage [54] Parallel Job Exe [12] SDSC Job Traces [33] Cost (in ratio) SPECjvm2008 [5] Price/Performance Ratio NAMD [40] Transaction Cost ($/job, $/Mbp/instance, milli-cents/operation, M$/1000 transactions) Dzero [38] MG-RAST + BLAST [37] Operate  Bridge metrics: The bridge metrics are not directly used for measuring the cost of Cloud services. As the type name suggests, they are normally used as bridges to contrast between costs of Cloud and inhouse resources in an "apple-to-apple" manner.…”

Section: ) Cost Evaluation Metrics (Cf Table Viii)mentioning

confidence: 99%

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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: ) Scalability Evaluation Metrics (Cf Table Vi)mentioning

confidence: 99%

Section: ) Cost Evaluation Metrics (Cf Table Viii)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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“…Despite the widespread use of FTBL, recent trends for automated lab experimentation and computational analysis pipelines (Dalman et al, 2010, 2016; Heux et al, 2017) call for contemporary model specification formats that are computationally easier to access and better verifiable than spreadsheets. Consequently, with our second generation 13 C MFA software 13CFLUX2 (Weitzel et al, 2013) an update to FTBL was proposed: the Flux Markup Language FluxML.…”

Section: A Content Standard For the Exchange Of Models And Data In 13mentioning

confidence: 99%

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

et al. 2019

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13 C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13 C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13 C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13 C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13 C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13 C MFA.

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“…The second type is to investigate sophisticated Cloud applications as real scientific cases, which is normally based on the aforementioned earlystage evaluation. For example, satellite image processing was studied on Windows Azure [11]; and a metabolic flux analysis was implemented by employing multiple types of Amazon services [3].…”

Section: Introductionmentioning

confidence: 99%

Early Observations on Performance of Google Compute Engine for Scientific Computing

O’Brien²,

Ranjan³

et al. 2013

2013 IEEE 5th International Conference on Cloud Computing Technology and Science

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Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCE's scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazon's EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.

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Metabolic Flux Analysis in the Cloud

Cited by 10 publications

References 14 publications

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

Early Observations on Performance of Google Compute Engine for Scientific Computing

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