[164] pinpoint

2022년 11월 30일 코엑스에�� 개최한 베스트콘2022(Better Software Testing Conference 2022)에서 발표한 강연 자료입니다. 대규모 장애를 막기 위해 소프트웨어/품질 엔지니어가 알아야 할 내결함성의 개념과 설계 기법을 공유드립니다. 생생한 강연 영상으로 확인해 보세요! https://youtu.be/OLsv7oG0VPo

NDC18에서 발표하였습니다. 현재 보고 계신 슬라이드는 1부 입니다.(총 2부) - 1부 링크: https://goo.gl/3v4DAa - 2부 링크: https://goo.gl/wpoZpY (SlideShare에 슬라이드 300장 제한으로 2부로 나누어 올렸습니다. 불편하시더라도 양해 부탁드립니다.)

JJUG CCC 2021 Fall の発表資料です。

AWSKRUG Winter Meetup 김명보 / VCNC

Airflow를 이용한 데이터 Workflow 관리

2023/08/15 Infcon2023에서 강연한 내용

1. The document discusses RESTful APIs and gRPC, comparing their characteristics and use cases. 2. RESTful APIs typically use HTTP and JSON to access resources via URLs while gRPC uses protocol buffers and HTTP/2 for efficient streaming and RPC. 3. gRPC is better suited for microservices and mobile apps due to its ability to handle streaming and performance, while REST is more widely used due to its simplicity and support in most languages.

JJUG CCC 2020 Fall での発表資料です。

KernelVM 探検隊 (2021/03/20) の発表資料『Linux の hugepage の開発動向』

OSC群馬2016

[236] 카카오의데이터파이프라인 윤도영

Meta/Facebook's database serving social workloads is running on top of MyRocks (MySQL on RocksDB). This means our performance and reliability depends a lot on RocksDB. Not just MyRocks, but also we have other important systems running on top of RocksDB. We have learned many lessons from operating and debugging RocksDB at scale. In this session, we will offer an overview of RocksDB, key differences from InnoDB, and share a few interesting lessons learned from production.

CA.go#1 (2017/06/21)の発表です golang pprofの解説をしました https://cyberagent.connpass.com/event/58120/

The document summarizes the new plugin API in Fluentd v0.14. Key points include: - The v0.12 plugin API was fragmented and difficult to write tests for. The v0.14 API provides a unified architecture. - The main plugin classes are Input, Filter, Output, Buffer, and plugins must subclass Fluent::Plugin::Base. - The Output plugin supports both buffered and non-buffered processing. Buffering can be configured by tags, time, or custom fields. - "Owned" plugins like Buffer are instantiated by primary plugins and can access owner resources. Storage is a new owned plugin for persistent storage. - New test drivers emulate plugin

Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다. 다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다. [Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안] Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 … [Apache Kafka 모니터링을 위한 Metrics 이해] Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다. 이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다. 모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임 [Apache Kafka 성능 Configuration 최적화] 성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다. 튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.

REEF is a meta-framework for big data analytics that eases development atop resource managers like YARN and Mesos. It provides a reusable control plane for coordinating data processing tasks and an adaptation layer for different resource managers. REEF decouples applications from cluster resources and handles common control plane functions like fault tolerance and configuration management. The framework is implemented in Java and C# and supports local, YARN, Mesos, and HDInsight execution environments. Future work includes graduating REEF from the Apache Incubator and using it to build new data processing frameworks and systems.

The document describes how to build a data science team and systems. It discusses establishing data collection and management systems, developing metrics and dashboards to analyze business data, creating predictive models using machine learning algorithms, and providing data science services like information retrieval to internal customers. The goal is to move from static, uncollected data to a fully realized big data platform and data science team that supports business analytics and decision making.

This document summarizes lessons learned from developing the Realm Android library. It discusses challenges such as setting up an Android library project, API design, testing, distribution methods, and issues like annotation processing, bytecode weaving, and native code support. Key points covered are how to start a library project, the importance of testing libraries extensively, and distribution options like Bintray.

This document summarizes a presentation about Packetbeat and monitoring distributed systems. It discusses how Packetbeat passively captures network packets, decodes protocols, and matches requests and responses to create JSON objects. It then sends this data to Elasticsearch for analysis. Aggregations like histograms, percentiles, and moving averages are used to analyze latency, identify slow methods, and detect anomalies in metrics over time. Other Beats like Topbeat, Filebeat, and Metricsbeat are also briefly introduced.

DEVIEW2015 DAY1. how riot works

DEVIEW2015 DAY1. 실전 스위프트 프로그래밍 - 김영후님

DEVIEW 2015 DAY1. 브라우저는 vsync를 어떻게 활용하고 있을까

DEVIEW DAY1. jpa와 모던 자바 데이터 저장 기술

DEVIEW2015 DAY1. react everywhere

DEVIEW2015 DAY1. 웹브라우저 감옥에서 살아남기

DEVIEW2015 DAY1. 네이버 효과툰 구현 이야기

MIT researchers have developed highly efficient quadruped robots like the Cheetah that can run at speeds up to 6m/s. The Cheetah uses a proprioceptive actuation system with high torque density motors to achieve high force control bandwidth over 120Hz. Its parallelized control system with multicore CPUs and FPGAs allows control frequencies up to 4kHz. Design principles for efficient legged locomotion include energy regeneration, low transmission impedance, and low leg inertia. The researchers are continuing their work with robots like Cheetah 2 and Hermes.

DRC-HUBO is Rainbow Robotics' humanoid robot that competed in the DRC Finals. It uses a modular, lightweight exoskeletal design with effective cooling and power systems. PODO-RT is the real-time framework that controls DRC-HUBO. It uses a distributed architecture with independent processes communicating over shared memory for high-speed control. DRC-HUBO demonstrated a variety of autonomous tasks at the DRC Finals, including driving, opening doors, using tools, and traversing rough terrain.

DEVIEW 2015 DAY1. rust

DEVIEW2015 DAY1. line on apple watch

DEVIEW 2015 DAY1. 네이버효과툰어떻게만들어졌나 - 김효님, 이현철님

DEVIEW2015 DAY1.데이터 센터의 오픈 소스 open compute project (ocp)

DEVIEW DAY1. 모바일 혈액진단기기 개발 삽질기

DEVIEW DAY1. mobile앱에서 효율적인 storage 접근 방법

This document discusses providing immersive sound for virtual reality. It notes that sound is half the experience of immersion. While VR technology allows immersion in digital worlds, truly immersive sound requires binaural 3D audio rendering or recording. Binaural audio uses head-related transfer functions to simulate the sound reaching each ear, allowing localization of sounds in 3D space. However, interactive binaural recording and matching sounds to visual content in real-time pose technical challenges. The document demonstrates an implementation of immersive 3D binaural audio for VR.

Paper Review in korean

비디오/오디오를 스트리밍하기위한 TCP/UDP 및 RTSP/RTP 프로토콜 설명

Wireshark 사용법 및 L2~L4 Switch 공부

L2, L3 네트워크에 대한 on-site 기초 교육이 필요한 기업은 아래 주소로 연락 바랍니다. cmyoo@netmanias.com

MySQL PowerGroup Tech Seminar (2015.2) - 6.Fluentd with MySQL (by 이이구) - URL : cafe.naver.com/mysqlpg

모바일 서비스 환경에서 고려해야 하는 네트워크 기본에 대해 알아본다. Toast Cloud 바로가기 - http://cloud.toast.com/

Hadoop Security (in Korean) Presented by Jae-hwa Jeong, Apache Tajo committer and Senior researcher at Gruter, at DeView 2014, Sep. 30 in Seoul Korea

Klaytn API Service Klaytn 플랫폼에서 BApp을 개발하기 위해서는 서비스 개발 회사들이 직접 Klaytn Node를 운영하는 것을 요구하게 됩니다. 이는 서비스 본질에 집중하고 싶은 개발사의 목적과 부합하지 않고 다소 전문적인 인력/기술이 필요하다는 문제가 발생할 수 있습니다. 이러한 문제점을 해결하기 위해 Ground X가 개발중인 Klaytn API Service에 대해 소개합니다.

리눅스 pacemaker 기반의 High Availaiblity 구성방법에 대해 설명합니다. pacemaker를 사용하는 다른 리눅스 기반도 구성이 가능합니다. Pacemaker 기반 Linux High Availability 입문용으로는 적합하지 않을 수 있습니다. Pacemaker 기반 Linux High Availability를 한 번도 설치 및 구성을 하지 않은 리눅스 관리자라면 설치 문서를 먼저 참고하십시오. RHEL7 및 CentOS 7을 중심으로 레드햇 계열의 리눅스에 적합한 내용으로 작성되었습니다.

이더리움 연구회 정기 발표회, 세션1 - 이더리움 플랫폼 아키텍쳐에 대한 상세 설명

TCP는 신뢰할 수 있을까??

Radware Alteon Introduction - new GUI

많은 인터넷 업체들이 사용하고 있는 메시징 프로토콜 XMPP 자료 입니다.

xmpp protocol

Socket Programming Basic

오픈스택 멀티노드 설치 후기입니다.

TRACK 1 (5G Technology) 어려웠다, WebRTC를 활용한 360º 영상통화 트러블 슈팅 : 최서윤 KT

인프라 모니터링을 위한 시스템을 구축하고 운영하는 데 있어, 다이내믹한 인프라 변화는 어려움으로 다가오고 있습니다. 본 세션에서는 인프라를 운영하는 팀 혹은 운영자 관점에서 바라본 미래 지향적 인프라 모니터링 시스템의 방향성과 이를 구현하기 위해 필요한 구성들을 공유하고자 합니다. 목차 1. NHN 모니터링의 현재 2. 모니터링의 변화 3. 모니터링 방법론 4. 모니터링 절차 5. NHN 모니터링의 미래 대상 - 인프라를 운영하는 시스템 엔지니어 - 인프라 모니터링 시스템에 관심이 있는 분

The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler in IPVS, Linux Kernel

[215] Druid로 쉽고 빠르게 데이터 분석하기

[245]Papago Internals: 모델분석과 응용기술 개발

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[235]Wikipedia-scale Q&A

The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.

This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.

This document provides a summary of new datasets and papers related to computer vision tasks including object detection, image matting, person pose estimation, pedestrian detection, and person instance segmentation. A total of 8 papers and their associated datasets are listed with brief descriptions of the core contributions or techniques developed in each.

그림이 정상 출력되는 다음 링크의 자료를 확인해 주세요. https://www.slideshare.net/deview/233-network-load-balancing-maglev-hashing-scheduler-in-ipvs-linux-kernel

This document presents a formula for calculating the loss function J(θ) in machine learning models. The formula averages the negative log likelihood of the predicted probabilities being correct over all samples S, and includes a regularization term λ that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[224]네이버 검색과 개인화

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

The document discusses running a TensorFlow Serving (TFS) container using Docker. It shows commands to: 1. Pull the TFS Docker image from a repository 2. Define a script to configure and run the TFS container, specifying the model path, name, and port mapping 3. Run the script to start the TFS container exposing port 13377

The document discusses linear algebra concepts including: - Representing a system of linear equations as a matrix equation Ax = b where A is a coefficient matrix, x is a vector of unknowns, and b is a vector of constants. - Solving for the vector x that satisfies the matrix equation using linear algebra techniques such as row reduction. - Examples of matrix equations and their component vectors are shown.

This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.