TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms,
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MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
AmpereOne implements MTE with zero tag-storage memory overhead and single-digit performance impact while identifying application memory management as the main remaining bottleneck.
A mixed Thompson-sampling and global-UCB strategy for Whittle-index policies in restless multi-armed bandits applied to data-center VM scheduling for grid demand response outperforms pure TW and EXP4 baselines.
citing papers explorer
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TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
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Single-Language Evidence Is Insufficient for Automated Logging: A Multilingual Benchmark and Empirical Study with LLMs
MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
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Optimized Memory Tagging on AmpereOne Processors
AmpereOne implements MTE with zero tag-storage memory overhead and single-digit performance impact while identifying application memory management as the main remaining bottleneck.
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Robust Restless Multi-Armed Bandit for Data Center Flexibility Services Through Virtual Machine Scheduling
A mixed Thompson-sampling and global-UCB strategy for Whittle-index policies in restless multi-armed bandits applied to data-center VM scheduling for grid demand response outperforms pure TW and EXP4 baselines.