In several queueing systems, arriving tasks have both service and timing requirements, and depart whenever one of them is attained. In several applications, the partial work performed by the system on tasks during their stay is still useful, even if the task is not completed. We analyze these partial service queues with abandonment under a many-server setting and characterize the equilibrium performance of several policies through fluid models. In particular, we show that deadline-oblivious policies like Least-Attained-Service and Last-Come-First-Served exhibit the same equilibrium performance as Earliest-Deadline-First. This striking property means that designers may avoid the difficult job of estimating deadlines without performance penalties. The conclusions are validated by numerical experiments.
Rule-based cloud security posture management (CSPM) solutions are known to produce a lot of false positives based on the limited contextual understanding and dependence on static heuristics testing. This paper introduces a validationdriven methodology that integrates active behavioral testing in cloud security posture management solution(s) to evaluate the exploitability of policy violations in real time. The proposed system employs lightweight and automated probes, built from open-source tools, validation scripts, and penetration testing test cases, to simulate adversarial attacks on misconfigured or vulnerable cloud assets without any impact to the cloud services or environment. For instance, cloud services may be flagged as publicly exposed and vulnerable despite being protected by access control layers, or secure policies, resulting in non-actionable alerts that consume analysts' time during manual validation. Through controlled experimentation in a reproducible AWS setup, we evaluated the reduction in false positive rates across various misconfiguration and vulnerable alerts. Our findings indicate an average reduction of 93% in false positives. Furthermore, the framework demonstrates low latency performance. These results demonstrate a scalable method to improve detection accuracy and analyse productivity in large cloud environments. While our evaluation focuses on AWS, the architecture is modular and extensible to multi-cloud setups.
Quantization reduces memory, computation, and communication costs by converting high-precision values into compact representations. Recent adaptive stochastic quantization (ASQ) methods optimize quantization values for a specific input vector, but they do not exploit shared randomness available to both the quantizer and the dequantizer. We introduce adaptive unbiased quantization (AUQ), which extends ASQ with shared randomness and can asymptotically improve achievable accuracy without increasing the encoded size. We then present Simba, a practical AUQ method that uses multiple sets of quantization values and selects among them using shared randomness while preserving unbiasedness. Simba starts by computing a fast approximate ASQ solution and refines it through a structured shared-randomness search. Across common synthetic distributions, Simba is up to about 24× more accurate than the state-of-the-art optimal ASQ method and can be up to two orders of magnitude faster. We also show that Simba can improve QUIC-FL by replacing its solver-generated lookup tables with tables computed by Simba.
Large language models (LLMs) are promising for domain-specific agents, but adapting them to network management remains difficult because existing specialization methods depend on large amounts of high-quality task data, while real network queries are diverse and unpredictable. To address this, we propose MeshAgent, a workflow that extracts reusable domain invariants from sample queries and encodes them as constraints to guide LLM generation and validation. Across three network management applications and a user study with industry professionals, MeshAgent consistently improves accuracy and reliability, including the ability to abstain when confidence is low. MeshAgent achieves over 95% accuracy, reaches 100% when combined with fine-tuned agents, and improves accuracy by up to 26% over baselines. These results show that constraint-based adaptation is a practical and scalable alternative to data-heavy LLM specialization for network management.
We propose using Fleming-Viot (FV) particle systems to estimate small exceedance probabilities of the electricity grid frequency for the dynamic dimensioning of frequency containment reserves (FCR). The methodology is proposed as a simpler alternative to importance sampling for diffusion processes, which generally requires the optimization of a functional. Under equal sample sizes, the FV estimator can reliably estimate probabilities that are smaller than those estimated with vanilla Monte Carlo by a factor of 10 -4 .
