OrcBench: A Representative Serverless Benchmark

Hancock, Ryan; Udayashankar, Sreeharsha; Mashtizadeh, Ali José; Al-Kiswany, Samer

doi:10.1109/cloud55607.2022.00028

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…It also generates usage log data for each call with a label denoting whether the traffic is part of a bot or legitimate. This data could be used in future research to differentiate legitimate traffic from botnet-generated traffic [8] . The purpose of this tool is the generation of datasets that contain function invocations on different cloud platforms such as AWS Lambda, IBM functions, Google Functions and Azure Functions and analyze if these invocations are botnet origin for DoW detection research.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Generation of a dataset for DoW attack detection in serverless architectures

Ortega Candel,

Mora Gimeno,

Mora Mora

2024

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…With the aim of obtaining the most relevant metrics to be added to the dataset, we have used other studies that analyse the main metrics for measuring the performance of workloads on serverless infrastructure [5] , [6] , [7] , [8] . Among the main metrics analysed we can highlight: Communications performance .…”

Section: Data Descriptionmentioning

confidence: 99%

Generation of a dataset for DoW attack detection in serverless architectures

Ortega Candel,

Mora Gimeno,

Mora Mora

2024

Data in Brief

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“…To overcome this problem of partly-documented experiments, a checklist is proposed in the next section which gives an answer to RQ2.2. [136] 2019 Simulation-as-a-Service with Serverless Computing Malla & Christensen [169] 2019 HPC in the cloud: Performance comparison of function as a service (FaaS) vs infrastructure as a service (IaaS) Malawski et al [168] 2020 Serverless execution of scientific workflows: Experiments with HyperFlow, AWS Lambda and Google Cloud Functions Sadaqat et al [237] 2022 Benchmarking Serverless Computing: Performance and Usability de Carvalho & de Araújo [38] 2022 Orama: A Benchmark Framework for Function-as-a-Service Hancock et al [95] 2022 OrcBench: A Representative Serverless Benchmark Palepu et al [208] 2022 Benchmarking the Data Layer Across Serverless Platforms…”

Section: Introductionmentioning

confidence: 99%

“…They tested the data transfer rates on different FaaS platforms for different data storage solutions. The last work in this category is early work on Or-cBench [95]. The focus here is to cluster execution traces from an Microsoft Azure dataset with 52,000 functions and 8.8 billion invocations and reengineer common execution time and invocation pattern.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Framework for Function as a Service

Manner

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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