DeLag: Detecting Latency Degradation Patterns in Service-based Systems

Overview

DeLag: Detecting Latency Degradation Patterns in Service-based Systems

Replication package of the work "DeLag: Detecting Latency Degradation Patterns in Service-based Systems".

Requirements

  • Python 3.6
  • Java 8
  • Apache Spark 2.3.1 (set $SPARK_HOME env variable with the folder path))
  • Elasticsearch for Spark 2.X 7.6.0 (set $ES_SPARK env variable with the jar path)
  • Maven 3.6.0 (only for datasets generation)
  • Docker 18.03 (only for datasets generation)

Use the following command to install Python dependencies

pip install --upgrade pip
pip install -r requirements.txt

The generation of datasets and the experimentation of techniques were performed on a dual Intel Xeon CPU E5-2650 v3 at 2.30GHz, totaling 40 cores and 80GB of RAM. We recommend to run the scripts of this replication package on a machine with similar specs.

Datasets

The datasets folder contains the datasets of traces used in the evaluation (in parquet format). Each row of each dataset represents a request and contains:

  • traceId: the ID of the request:
  • [requestLatency]: the overall latency of the request. It is represented by the column ts-travel-service_queryInfo in the Train-Ticket case study and by the column HomeControllerHome in the E-Shopper case study.
  • experiment: if equal to 0 (resp. 1) the request is affected by the ADC (resp. ) otherwise is not affected by any ADCs.
  • [RPC]: the cumulative execution time of [RPC] within the request.

Datasets generation

The datasets-generation folder contains the bash scripts used to generate the datasets used in the evaluation.

Techniques

The techniques folder contains the implementations of DeLag, CoTr, KrSa and DeCaf. In the following you can find the main Python classes used to implement each technique:

  • DeLag: class GeneticRangeAnalysis
  • CoTr: classes RangeAnalysis and GA
  • KrSa: classes RangeAnalysis and BranchAndBound
  • DeCaf: class DeCaf.

Experiments

The experiments folder contains the Python scripts used to execute DeLag and baselines techniques on the generated datasets.

Results

The results folder contains the results of our experimentation. Each row of each csv file represents a run of a particural technique on a dataset and contains:

  • exp: the dataset ID.
  • algo: the technique experimented. The notation used to indicate each techique is described below:
    • gra: DeLag - DeLag: Detecting Latency Degradation Patterns in Service-based Systems
    • bnb: KrSa - Understanding Latency Variations of Black Box Services (WWW 2013)
    • ga: CoTr - Detecting Latency Degradation Patterns in Service-based Systems (ICPE 2020)
    • decaf DeCaf - DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services (ICSE 2020)
    • kmeans: K-means
    • hierarchical: HC - Hierachical clustering
  • trial: the ID of the run (techniques may be repeated multiple times on a dataset to mitigate result variabilility)
  • precision: effectiveness measure - Precision ()
  • recall: effectiveness measure - Recall ()
  • fmeasure: effectiveness measure - F1-score ()
  • time: execution time in seconds

Scripts

The scripts folder contains the Python scripts used to generate the figures and tables of the paper.

Systems

The systems folder contains the two case study systems.

You might also like...
McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns
McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns

DiffuseAnimals: Reaction-Diffusion Models for the Generation of Biological Patterns Introduction Reaction-diffusion equations can be utilized in order

Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

Manga Character Screentone Synthesis Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters" presented in IEEE ISM 2

DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

Architecture Patterns with Python (TDD, DDD, EDM)

architecture-traning Architecture Patterns with Python (TDD, DDD, EDM) Chapter 5. 높은 기어비와 낮은 기어비의 TDD 5.2 도메인 계층 테스트를 서비스 계층으로 옮겨야 하는가? 도메인 계층 테스트 def

A DeepStack custom model for detecting common objects in dark/night images and videos.
A DeepStack custom model for detecting common objects in dark/night images and videos.

DeepStack_ExDark This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for d

A custom DeepStack model for detecting 16 human actions.
A custom DeepStack model for detecting 16 human actions.

DeepStack_ActionNET This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API fo

Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label.
Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label.

Tensorflow-Mobile-Generic-Object-Localizer Python Tensorflow 2 scripts for detecting objects of any class in an image without knowing their label. Ori

Python TFLite scripts for detecting objects of any class in an image without knowing their label.
Python TFLite scripts for detecting objects of any class in an image without knowing their label.

Python TFLite scripts for detecting objects of any class in an image without knowing their label.

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples This project is for the paper "Training Confidence-Calibrated Clas

Releases(v1.1)
  • v1.1(Dec 22, 2022)

    Replication package of the work "DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-based Systems"

    Source code(tar.gz)
    Source code(zip)
Owner
SEALABQualityGroup @ University of L'Aquila
SEALABQualityGroup @ University of L'Aquila
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Torch-template-for-deep-learning Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and

Li Shengyan 270 Dec 31, 2022
Code for the bachelors-thesis flaky fault localization

Flaky_Fault_Localization Scripts for the Bachelors-Thesis: "Flaky Fault Localization" by Christian Kasberger. The thesis examines the usefulness of sp

Christian Kasberger 1 Oct 26, 2021
Introduction to CPM

CPM CPM is an open-source program on large-scale pre-trained models, which is conducted by Beijing Academy of Artificial Intelligence and Tsinghua Uni

Tsinghua AI 136 Dec 23, 2022
This project hosts the code for implementing the ISAL algorithm for object detection and image classification

Influence Selection for Active Learning (ISAL) This project hosts the code for implementing the ISAL algorithm for object detection and image classifi

25 Sep 11, 2022
Not Suitable for Work (NSFW) classification using deep neural network Caffe models.

Open nsfw model This repo contains code for running Not Suitable for Work (NSFW) classification deep neural network Caffe models. Please refer our blo

Yahoo 5.6k Jan 05, 2023
Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

The official code for the paper "Inverse Problems Leveraging Pre-trained Contrastive Representations" (to appear in NeurIPS 2021).

Sriram Ravula 26 Dec 10, 2022
Power Core Simulator!

Power Core Simulator Power Core Simulator is a simulator based off the Roblox game "Pinewood Builders Computer Core". In this simulator, you can choos

BananaJeans 1 Nov 13, 2021
🤗 Paper Style Guide

🤗 Paper Style Guide (Work in progress, send a PR!) Libraries to Know booktabs natbib cleveref Either seaborn, plotly or altair for graphs algorithmic

Hugging Face 66 Dec 12, 2022
Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

numbalsoda numbalsoda is a python wrapper to the LSODA method in ODEPACK, which is for solving ordinary differential equation initial value problems.

Nick Wogan 52 Jan 09, 2023
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 82 Dec 24, 2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight)

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

THUML @ Tsinghua University 221 Dec 31, 2022
The spiritual successor to knockknock for PyTorch Lightning, get notified when your training ends

Who's there? The spiritual successor to knockknock for PyTorch Lightning, to get a notification when your training is complete or when it crashes duri

twsl 70 Oct 06, 2022
Learning from graph data using Keras

Steps to run = Download the cora dataset from this link : https://linqs.soe.ucsc.edu/data unzip the files in the folder input/cora cd code python eda

Mansar Youness 64 Nov 16, 2022
Research code for Arxiv paper "Camera Motion Agnostic 3D Human Pose Estimation"

GMR(Camera Motion Agnostic 3D Human Pose Estimation) This repo provides the source code of our arXiv paper: Seong Hyun Kim, Sunwon Jeong, Sungbum Park

Seong Hyun Kim 1 Feb 07, 2022
[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"

Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu

Jiawei Ren 65 Dec 21, 2022
Development Kit for the SoccerNet Challenge

SoccerNetv2-DevKit Welcome to the SoccerNet-V2 Development Kit for the SoccerNet Benchmark and Challenge. This kit is meant as a help to get started w

Silvio Giancola 117 Dec 30, 2022
Learn about Spice.ai with in-depth samples

Samples Learn about Spice.ai with in-depth samples ServerOps - Learn when to run server maintainance during periods of low load Gardener - Intelligent

Spice.ai 16 Mar 23, 2022
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop

8 Mar 11, 2022