Quantitative Analysis of Cloud Function Evolution in the AWS Serverless Application Repository

Spillner, Josef

doi:10.48550/arxiv.1905.04800

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…In this subsection, the Technical sub-aspects of the composition and decomposition of IoT capabilities are discussed. Domains of application, stakeholders' concerns, and realworld implementations (e.g., AWS GreenGrass + Lambda [126][110] [28]) or efforts that provide substantial code-base or interesting prototypes (e.g., MCC Cloudlets [30]) were explored. IoT platforms for service composition and related communication protocols, data models, schemas, and engines will be discussed.…”

Section: Technical Aspectmentioning

confidence: 99%

“…AWS Greengrass [126] A commercial cloud solution that provides various services, including storage and computing.…”

Section: N/amentioning

confidence: 99%

“…Thing Orchestration [191], along with Cloud CAMP [132] are two other examples of composition performed at the cloud level. Another example of the use of cloud computing capabilities to compose novel services or applications can be found in both commercial solutions: AWS GreenGrass [126], and Microsoft Azure IoT [129] [128]. AWS GreenGrass leverages Lambda scripts to compose value-added services and appli-cations by connecting different AWS services; this requires the installation of the different GreenGrass dependencies and certificates related to the target devices and services.…”

Section: ) Aq5: What Are the Different Stakeholders' Categories And C...mentioning

confidence: 99%

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IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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As billions of IoT devices join the Internet, researchers and innovators increasingly explore IoT capabilities achieved via service composition or reuse of existing capabilities via service decomposition. Many systematic literature reviews (SLRs) were produced on this subject; however, two issues remain to be addressed: i) a reference taxonomy of the different aspects of IoT capabilities composition and decomposition is needed, and ii) many formal questions (e.g., standards role, formal representations applications, state-space explosion countermeasures, etc.), technical questions (e.g., composition process types and automation levels synergies, service decomposition categories, the role of AI/ML, etc.), and QoS questions (e.g., privacy, interoperability, and scalability challenges and solutions, etc.) remain unanswered. We introduce this work by discussing notions of IoT capabilities composition and decomposition in a layered IoT architecture while highlighting the strengths and weaknesses of existing SLRs. We identify unanswered questions through gaps in related work and motivate these questions using the PICOC methodology. We explain the search methodology and organize the topic questions using the proposed reference taxonomy. The identified research questions are answered, and trends and gaps that need additional attention from the research community are highlighted. This effort benefits city planners and end-users of IoT systems as it contributes to a better understanding of the role of composition and decomposition of IoT capabilities in building valueadded services or reusing existing ones for resource optimization. For researchers, this effort contributes a reference taxonomy for the topic and sheds light on important questions while highlighting corresponding trends and gaps requiring further attention.

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Section: Technical Aspectmentioning

confidence: 99%

“…AWS Greengrass [126] A commercial cloud solution that provides various services, including storage and computing.…”

Section: N/amentioning

confidence: 99%

Section: ) Aq5: What Are the Different Stakeholders' Categories And C...mentioning

confidence: 99%

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IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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“…Another class of studies looks at the ways developers are using FaaS offerings, by looking at public application repositories [33]. While valuable, this approach cannot offer insights on the aggregate workload seen by a provider.…”

Section: Related Workmentioning

confidence: 99%

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

Shahrad,

Fonseca,

Goiri

et al. 2020

Preprint

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Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud. This paradigm shifts the complexity of allocating and provisioning resources to the cloud provider, which has to provide the illusion of always-available resources (i.e., fast function invocations without cold starts) at the lowest possible resource cost. Doing so requires the provider to deeply understand the characteristics of the FaaS workload. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we first characterize the entire production FaaS workload from Microsoft Azure Functions. We show for example that most functions are invoked very infrequently, but there is an 8-order-of-magnitude range of invocation frequencies. Using observations from our characterization, we then propose a practical resource management policy that significantly reduces the number of function cold starts, while spending fewer resources than stateof-the-practice policies.

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“…The chosen function orchestrators are reviewed based on the set of defined criteria, and their runtime overhead is evaluated experimentally. Spillner [74] investigates how FaaS-based applications are specified, stored, and offered using the AWS Serverless Applications Repository. Various statistics are presented related to different aspects of functions' lifecycle.…”

Section: Related Workmentioning

confidence: 99%

FaaSten Your Decisions: Classification Framework and Technology Review of Function-as-a-Service Platforms

Yussupov¹,

Soldani²,

Breitenbücher³

et al. 2020

Preprint

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Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, hereby included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a mixed method study and (ii) a systematic technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. Moreover, we present (iii) a FaaS platform selection support system, called FaaStener, which helps researchers and practitioners to choose the FaaS platform most suited for their requirements.

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Quantitative Analysis of Cloud Function Evolution in the AWS Serverless Application Repository

Cited by 3 publications

References 12 publications

IoT Capabilities Composition and Decomposition: A Systematic Review

IoT Capabilities Composition and Decomposition: A Systematic Review

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider

FaaSten Your Decisions: Classification Framework and Technology Review of Function-as-a-Service Platforms

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