Sizeless

Eismann, Simon; Bui, Long; Grohmann, Johannes; Abad, Cristina L.; Herbst, Nikolas; Kounev, Samuel

doi:10.1145/3464298.3493398

Cited by 38 publications

(13 citation statements)

References 23 publications

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“…Cloud function instances allocated with a multi-core configuration are only fully utilized when deploying multi-threaded source code. This is an important aspect for a simulation approach and often misinterpreted in research [17,48,70,72,154,189] as emphasized by the third contribution (c3) of this work. As already said in Section 3.1.5, AWS Lambda was the first public cloud provider offering for cloud functions.…”

Section: Resource Scaling Strategiesmentioning

confidence: 94%

“…Bluemix was the former name of IBM's cloud offering and OpenWhisk was chosen since IBM Cloud Functions are a provider managed deployment of Apache OpenWhisk, https://rabbah.io/openwhisk.html. 70 https://cloud.google.com/functions/docs/release-notes 71 https://support.huaweicloud.com/intl/en-us/wtsnew-functiongraph/index.html started in April 2017 72 the situation is similar when looking for example at a publication of WANG and others [280]. The last included Asian FaaS platform Baidu Cloud FunctionCompute 73 was not considered in the following since the website was not available in English at the time of writing.…”

Section: Faas Offerings Over Timementioning

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: Resource Scaling Strategiesmentioning

confidence: 94%

Section: Faas Offerings Over Timementioning

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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“…However, a limitation of this approach is that it only applies to functions invoked on the AWS platform at least 50 times in the past two weeks. Sizeless [ 9 ] builds a multi-objective regression model that captures the runtime information of a serverless function under a specific memory configuration as input. The model then predicts the execution time of this function in other memory configurations, aiming to select the optimal configuration that minimizes cost while satisfying latency constraints.…”

Section: Related Workmentioning

confidence: 99%

“…However, due to the black box nature of the serverless platform, it is extremely challenging for developers to determine the optimal configuration of each function within an application. A recent survey indicated that a staggering 47% of serverless functions in production adopt default memory sizes [ 9 ], indicating that developers often neglect resource sizing, resulting in avoidable extra costs.…”

Section: Introductionmentioning

confidence: 99%

“…The existing solutions for configuring resources in serverless applications can be classified into two principal categories. The first category entails modeling performance based on historical datasets of functions, which can be used to predict the execution time of functions under diverse configurations, thereby facilitating the selection of an appropriate configuration plan [ 9 , 10 , 11 ]. However, this category of solutions is limited due to its reliance on the availability of extensive historical data, and fails to address data scarcity when new functions are submitted.…”

Section: Introductionmentioning

confidence: 99%

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FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

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faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

Raith,

Rausch,

Furutanpey

et al. 2023

Softw Pract Exp

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This paper presents faas‐sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim. By running experiments on both real‐world test beds and replicating them using faas‐sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.

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Sizeless

Cited by 38 publications

References 23 publications

A Simulation Framework for Function as a Service

A Simulation Framework for Function as a Service

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

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