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AI-Enabled Systems: Reliability and Scalability Gaps Identified in New Study

By PulseAugur Editorial · [1 sources] ·

A systematic mapping study analyzed the reliability and scalability of cloud-native systems, particularly those incorporating AI workloads. The research found that while production deployments exist for certain AI-enabled system aspects like failure prevention and autoscaling, a significant gap remains in production evidence for edge-native systems and dependable AI agent operations. The study identified a reliance on simulations and testbeds for edge reliability and physical AI, with agent frameworks generally proving fragile in empirical studies. AI

IMPACT Highlights critical gaps in production evidence for edge-native systems and AI agent reliability, guiding future research and development.

RANK_REASON The item is a systematic mapping study published on arXiv, detailing research findings on cloud-native and AI-enabled systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI-Enabled Systems: Reliability and Scalability Gaps Identified in New Study

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The item is a systematic mapping study published on arXiv, detailing research findings on cloud-native and AI-enabled systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Shrinivass Arunachalam Balasubramanian ·

    Advancements in Cloud-Native Systems: A Systematic Mapping Study of Reliability and Scalability across Cloud-Native, Edge-Native and AI-Enabled Systems

    Cloud-native systems now extend to the network edge and host artificial-intelligence (AI) workloads, including AI agents, but evidence on their reliability and scalability is scattered and uneven. We report a PRISMA 2020 systematic mapping study with an evidence-graded synthesis.…

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