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This paper articulates our vision for a learning-based untrustworthy distributed database. We focus on permissioned blockchain systems as an emerging instance of untrustworthy distributed databases and argue that as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof permissioned blockchain systems need to be designed withfull-stack adaptivityin mind. At the application level, a future-proof system must adaptively learn the best-performing transaction processing paradigm and quickly adapt to new hardware and unanticipated workload changes on the fly. Likewise, the Byzantine consensus layer must dynamically adjust itself to the workloads, faulty conditions, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level, cloud providers must enable cross-layer adaptation, which identifies performance bottlenecks and possible attacks, and determines at runtime the degree of resource disaggregation that best meets application requirements. Within this vision of the future, our paper outlines several research challenges together with some preliminary approaches.
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This content will become publicly available on June 1, 2026
The bottlenecks of AI: challenges for embedded and real-time research in a data-centric age
Abstract Recent advances in AI culminate a shift in science and engineering away from strong reliance on algorithmic and symbolic knowledge towards new data-driven approaches. How does the emerging intelligent data-centric world impact research on real-time and embedded computing? We argue for two effects: (1) new challenges in embedded system contexts, and (2) new opportunities for community expansion beyond the embedded domain. First,on the embedded system side, the shifting nature of computing towardsdata-centricityaffects the types of bottlenecks that arise. At training time, the bottlenecks are generallydata-related. Embedded computing relies onscarcesensor data modalities, unlike those commonly addressed in mainstream AI, necessitating solutions forefficient learningfrom scarce sensor data. At inference time, the bottlenecks areresource-related, calling forimproved resource economyandnovel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additionalmodel-relatedchallenges in embedded contexts. Second,on the domain expansion side, we argue that community expertise in handling resource bottlenecks is becoming increasingly relevant to a new domain: thecloudenvironment, driven by AI needs. The paper discusses the novel research directions that arise in the data-centric world of AI, covering data-, resource-, and model-related challenges in embedded systems as well as new opportunities in the cloud domain.
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- Award ID(s):
- 2038817
- PAR ID:
- 10642809
- Publisher / Repository:
- Journal of Real-time Systems, Springer
- Date Published:
- Journal Name:
- Real-Time Systems
- Volume:
- 61
- Issue:
- 2
- ISSN:
- 0922-6443
- Page Range / eLocation ID:
- 185 to 236
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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