Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective

• URL: http://arxiv.org/abs/2412.16888v2
• Date: Thu, 02 Jan 2025 06:55:38 GMT
• Title: Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective
• Authors: Mingyu Huang, Peili Mao, Ke Li,
• Abstract summary: We advocate a novel perspective to rethink performance analysis -- modeling the configuration space as a structured landscape''<n>We apply our framework to $86$M benchmarked configurations from $32$ running workloads of $3$ real-world systems.
• Score: 3.845572815195074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the controllability and tuning of the underlying system, yet has long been a dark hole of knowledge due to its black-box nature. While there have been previous efforts in performance analysis for these systems, they analyze the configurations as isolated data points without considering their inherent spatial relationships. This renders them incapable of interrogating many important aspects of the configuration space like local optima. In this work, we advocate a novel perspective to rethink performance analysis -- modeling the configuration space as a structured ``landscape''. To support this proposition, we designed \our, an open-source, graph data mining empowered fitness landscape analysis (FLA) framework. By applying this framework to $86$M benchmarked configurations from $32$ running workloads of $3$ real-world systems, we arrived at $6$ main findings, which together constitute a holistic picture of the landscape topography, with thorough discussions about their implications on both configuration tuning and performance modeling.

Related papers

• Spatial Understanding from Videos: Structured Prompts Meet Simulation Data [79.52833996220059]
  We present a unified framework for enhancing 3D spatial reasoning in pre-trained vision-language models without modifying their architecture.<n>This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes.
  arXiv  Detail & Related papers  (2025-06-04T07:36:33Z)
• Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes [50.23625950905638]
  Mesh saliency enhances the adaptability of 3D vision by identifying and emphasizing regions that naturally attract visual attention. We introduce mesh Mamba, a unified saliency prediction model based on a state space model (SSM) Mesh Mamba effectively analyzes the geometric structure of the mesh while seamlessly incorporating texture features into the topological framework.
  arXiv  Detail & Related papers  (2025-04-02T08:22:25Z)
• Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems [0.0]
  Sentiment analysis is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. This paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations.
  arXiv  Detail & Related papers  (2025-03-24T01:01:19Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Robust Incremental Structure-from-Motion with Hybrid Features [73.55745864762703]
  We introduce an incremental Structure-from-Motion (SfM) system that leverages lines and their structured geometric relations. Our system is consistently more robust and accurate compared to the widely used point-based state of the art in SfM.
  arXiv  Detail & Related papers  (2024-09-29T22:20:32Z)
• Deep Configuration Performance Learning: A Systematic Survey and Taxonomy [3.077531983369872]
  We conduct a comprehensive review on the topic of deep learning for performance learning of software, covering 1,206 searched papers spanning six indexing services. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data. We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field.
  arXiv  Detail & Related papers  (2024-03-05T21:05:16Z)
• 360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception [56.84921040837699]
  Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results. We propose an orthogonal plane disentanglement network (termed DOPNet) to distinguish ambiguous semantics. We also present an unsupervised adaptation technique tailored for horizon-depth and ratio representations. Our solution outperforms other SoTA models on both monocular layout estimation and multi-view layout estimation tasks.
  arXiv  Detail & Related papers  (2023-12-26T12:16:03Z)
• S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models [1.068128849363198]
  This study investigates the design choices within the broad category of encoder-predictor architectures. We identify robust architectures applicable to both time series and spectrogram input representations. These architectures incorporate structured state space models as integral components and achieve statistically significant performance improvements.
  arXiv  Detail & Related papers  (2023-10-10T15:42:14Z)
• LONViZ: Unboxing the black-box of Configurable Software Systems from a Complex Networks Perspective [9.770775293243934]
  This paper proposes a tool, dubbed LONViZ, to facilitate the exploratory analysis of black-boxconfigured software systems. In experiments, we choose four widely used real-world software systems to develop benchmark platforms under 42 different running environments. We find that LONViZ enables both qualitative and quantitative analysis and disclose various interesting hidden patterns and properties of different software systems.
  arXiv  Detail & Related papers  (2022-01-05T03:14:39Z)
• T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP [0.0]
  This paper presents the design and implementation of a visual analytic framework for assisting researchers in such process. Our framework offers an intuitive overview that allows the user to explore different facets of the model. It allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence.
  arXiv  Detail & Related papers  (2021-08-31T02:20:46Z)
• SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition [69.90530987240899]
  We present an unsupervised variational approach to this problem. Our model learns to infer two sets of latent representations from RGB video input alone. It represents object attributes in an allocentric manner which does not depend on viewpoint.
  arXiv  Detail & Related papers  (2021-06-07T17:59:23Z)
• S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
  We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures. We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
  arXiv  Detail & Related papers  (2020-07-13T17:44:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.