相关论文: Monitoring Extreme-scale Lustre Toolkit

We present Montre, a monitoring tool to search patterns specified by timed regular expressions over real-time behaviors. We use timed regular expressions as a compact, natural, and highly-expressive pattern specification language for…

This paper presents a tool, PELIB, developed in MATLAB /SIMULINK environment to perform pointing performance analysis based on European pointing standards. PELIB is designed as an extension of the Satellite Dynamics Toolbox (SDT), which…

The growing reliance on computer systems, particularly personal computers (PCs), necessitates heightened reliability to uphold user satisfaction. This research paper presents an in-depth analysis of extensive system telemetry data,…

Visualization, querying, statistical analysis and mid-long-term scheduling are common concerns for any observatory. HILTS is a Java tool developed for the Herschel project to address all these issues in a unified way.

IT environments typically have logging mechanisms to monitor system health and detect issues. However, the huge volume of generated logs makes manual inspection impractical, highlighting the importance of automated log analysis in IT…

The last twenty years have seen the development and popularity of network measurement infrastructures. Internet measurement platforms have become common and have demonstrated their relevance in Internet understanding and security…

WebAssembly (Wasm) has become a key compilation target for portable and efficient execution across diverse platforms. Benchmarking its performance, however, is a multi-dimensional challenge: it depends not only on the choice of runtime…

We present mstlo (mistletoe), a Rust library for high-performance online monitoring of signal temporal logic (STL), with Python bindings. The library provides: (i) a unified interface for multiple STL semantics, including Robust…

Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated. LLMs enable a new type of workload, agentic pipeline search, in which autonomous or semi-autonomous agents generate,…

This paper presents the Userspace Integrity Measurement Toolkit (USIM Toolkit), a set of integrity measurement collection tools capable of detecting advanced malware threats, such as memory-only implants, that evade many traditional…

Modern in-orbit satellites and other available remote sensing tools have generated a huge availability of public data waiting to be exploited in different formats hosted on different servers. In this context, ETL formalism becomes relevant…

Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, resulting in increased storage and bandwidth requirements for…

The rapid evolution of malware attacks calls for the development of innovative detection methods, especially in resource-constrained edge computing. Traditional detection techniques struggle to keep up with modern malware's sophistication…

Performance profiles of GPU kernels generated by tools such as Nsight Compute are rich in detail but are often challenging to interpret. To achieve the best performance possible on a given GPU architecture, kernel developers need to spend…

Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the actual reasons (i.e.,…

This paper reports on the design and implementation of the HPC performance monitoring system deployed to continuously monitor performance metrics of all jobs on the HPC systems at the Max Planck Computing and Data Facility (MPCDF). Thereby…

Linting tools automatically identify source code fragments that do not follow a set of predefined standards. Such feedback tools are equally desirable for "linting" agile development processes. However, providing concrete feedback on…

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been…

This paper presents LITE, an LLM-based evaluation method designed for efficient and flexible assessment of taxonomy quality. To address challenges in large-scale taxonomy evaluation, such as efficiency, fairness, and consistency, LITE…

Large language models (LLMs) have limitations in handling tasks that require real-time access to external APIs. While several benchmarks like ToolBench and APIGen have been developed to assess LLMs' API-use capabilities, they often suffer…