Edge Intelligence as a Service

Raith, Philipp; Dustdar, Schahram

doi:10.1109/scc53864.2021.00038

Cited by 9 publications

(3 citation statements)

References 65 publications

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“…Edge Intelligence emphasizes and requires the execution of applications across the edge‐cloud continuum. Applications may require ultra‐low latency (e.g., < 10 milliseconds), have strict privacy constraints (e.g., health data), and are formulated as complex workflows consisting of multiple applications, each having different requirements (e.g., AI pipelines) 31 . Serverless adaptations are not only based on requirements but must consider environmental changes.…”

Section: Simulation‐driven Serverless Edge Computing Designmentioning

confidence: 99%

“…Applications may require ultra-low latency (e.g., < 10 milliseconds), have strict privacy constraints (e.g., health data), and are formulated as complex workflows consisting of multiple applications, each having different requirements (e.g., AI pipelines). 31 Serverless adaptations are not only based on requirements but must consider environmental changes. A key characteristic of the edge-cloud continuum is its dynamic and heterogeneous environment.…”

Section: Serverless Edge Computingmentioning

confidence: 99%

“…Complex AI pipelines are a prime example in which not only different hardware accelerators must take into account but also dynamic application flows can change the path of execution (i.e., which functions must be executed). 31,37 We can split the challenges from the heterogeneous infrastructure into node-level and topology-level. Nodes offer different capabilities and capacities.…”

Section: Serverless Function Adaptation Strategiesmentioning

confidence: 99%

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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

Self Cite

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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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Section: Simulation‐driven Serverless Edge Computing Designmentioning

confidence: 99%

Section: Serverless Edge Computingmentioning

confidence: 99%

Section: Serverless Function Adaptation Strategiesmentioning

confidence: 99%

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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

Self Cite

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Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

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PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

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SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions Through Co-Simulation

Raith,

Nastic,

Dustdar

2024

2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

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Edge Intelligence as a Service

Cited by 9 publications

References 65 publications

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

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

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions Through Co-Simulation

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