An empirical study of two production edge platforms using latency-sensitive cryptocurrency analytics workloads, conducted from a local Southeast-Asian client and three supplemental AWS cloud clients, reveals that platform-level latency advantages are dominated by client-edge proximity rather than systemic runtime differences.
Organizations processing historical datasets with billions of rows face a fundamental architectural choice: move data to computation (the traditional query-centric approach) or move computation to data. This paper presents the Computation-Proximate Pattern, an architecture that combines actor-based in-memory computatio...
A. Jaime· Distributed and parallel dat...· 0 citations
SACC is the first to treat probabilistic finality as a quantifiable risk budget, and consistently outperforms traditional 2PC and modern fast-path protocols in challenging environments, significantly improving commit rates while strictly bounding tail latency.
A controlled characterization framework that evaluates independently deployed CU and DU functions under matched hardware and traffic conditions and identify distinct CU and DU execution characteristics and motivate function-specific processor analysis and optimization is described.
Moojan Kamalzadeh, Larry J. Horner, Linqi Xiao et al.· 0 citations
Accuracy on IoTMal-2026, matching or exceeding Deep SVDD, Deep SAD, and Kitsune under an identical protocol is evaluated, finding the model’s bidirectional recurrent component does not reliably improve mean accuracy over a simpler, convolution-only alternative.
This paper examines the impact of migrating large-scale data processing frameworks from bare-metal servers to cloud-native environments on system performance. Specifically, we compare traditional Hadoop MapReduce deployed on bare-metal infrastructure with Apache Spark running on Kubernetes, using a 200 GB TeraSort work...
Habiba Ben Abderrahmane, Yu-Nan Li, Assia Ben-Khelifa et al.· IEEE Access· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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