Modeling and Extracting Load Intensity Profiles

Kistowski, Jóakim von; Herbst, Nikolas; Zöller, Daniel; Kounev, Samuel; Hotho, Andreas

doi:10.1109/seams.2015.19

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…The most important information is the hardware used, the software stack and its configuration, the workload generator and pattern etc. This precise documentation is the foundation for repeatable experiments [279]. Literature studies reveal a huge discrepancy between this desired state and the reality of published experiments.…”

Section: Relevantmentioning

confidence: 93%

“…To assess different SUTs and make benchmarks comparable to each other, it is important to have quality criteria in mind during the design phase, especially when thinking about the benchmark methodology. Different criteria were defined in literature [23,80,103,279] and a selection is presented in the following. These criteria also influence the selection of suitable metrics and workloads to support the intended benchmark goals.…”

Section: Quality Criteria For Experimental Benchmark Designmentioning

confidence: 99%

“…The second aspect especially hampers the interpretation when redoing the experiment on own hardware which typically diverges from the hardware used to perform the original experiment. When conducting reproducibility studies, the concrete value of specific metrics is not in focus but the same objective and conclusion from the experiment [279]. Published raw data helps to assess the difference and enables researchers to investigate whether these are introduced by a slightly different setup or whether the original published data has some flaws limiting repeatability.…”

Section: Relevantmentioning

confidence: 99%

“…Valid entries limit the number of possible configurations which increases the validity of results. These aspects should be documented but also asserted to prevent benchmarks being executed with invalid values [103,279]. For example, the resource limitations when starting a SUT has an impact on the execution behavior of the system.…”

Section: Fairmentioning

confidence: 99%

“…Nevertheless, being economical is intuitive since the trade-off between financial investments and information gain is already inherent in the criterion name. One example which is often considered in literature [103,279] is the monetary expenditure conducting the already mentioned TPC-C benchmark. TPC-C results are ordered by the maximum throughput measured in transactions per minute for benchmark C -tpmC.…”

Section: Fairmentioning

confidence: 99%

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A Simulation Framework for Function as a Service

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2024

Schriften Aus Der Fakultät Wirtschaftsinformatik Und Angewandte Informatik Der Otto-Friedrich-Universität Bamberg

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Serverless Computing is seen as a game changer in operating large-scale applications. While practitioners and researches often use this term, the concept they actually want to refer to is Function as a Service (FaaS). In this new service model, a user deploys only single functions to cloud platforms where the cloud provider deals with all operational concerns – this creates the notion of server-less computing for the user. Nonetheless, a few configurations for the cloud function are necessary for most commercial FaaS platforms as they influence the resource assignments like CPU time and memory. Due to these options, there is still an abstracted perception of servers for the FaaS user. The resource assignment and the different strategies to scale resources for public cloud offerings and on-premise hosted open-source platforms determine the runtime characteristics of cloud functions and are in the focus of this work. Compared to cloud offerings like Platform as a Service, two out of the five cloud computing characteristics improved. These two are rapid elasticity and measured service. FaaS is the first computational cloud model to scale functions only on demand. Due to an independent scaling and a strong isolation via virtualized environments, functions can be considered independent of other cloud functions. Therefore, noisy neighbor problems do not occur. The second characteristic, measured service, targets billing. FaaS platforms measure execution time on a millisecond basis and bill users accordingly based on the function configuration. This leads to new performance and cost trade-offs. Therefore, this thesis proposes a simulation approach to investigate this tradeoff in an early development phase. The alternative would be to deploy functions with varying configurations, analyze the execution data from several FaaS platforms and adjust the configuration. However, this alternative is time-consuming, tedious and costly. To provide a proper simulation, the development and production environment should be as similar as possible. This similarity is also known as dev-prod parity. Based on a new methodology to compare different virtualized environments, users of our simulation framework are able to execute functions on their machines and investigate the runtime characteristics for different function configurations at several cloud platforms without running their functions on the cloud platform at all. A visualization of the local simulations guide the user to choose an appropriate function configuration to resolve the mentioned trade-off dependent on their requirements.

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Section: Relevantmentioning

confidence: 93%