AppSphere 15 - Expedia Lessons from the Trenches: Managing AppDynamics at ScaleAppDynamics
AppDynamics is used extensively within Expedia to manage over 100 applications spread across three environments. The FCTS Quality & Operations (Q&O) team supports many services and applications in Expedia’s eCommerce Platform organization, including the AppDynamics implementation. To support AppDynamics users and use cases through out Expedia, the team has leveraged AppDynamics API's extensively to automate functions and improve troubleshooting capabilities.
In this session you will learn how the Expedia team uses AppDynamics at scale:
- Dynamically create, modify, and delete (and even deploy) health rules by using the AppD Health Rule Creation REST API to reduce management overhead
- Import AppDynamics transaction snapshot to Expedia simulation tools to get contextual visibility and troubleshooting data so that AppDev can quickly solve issues
- Best practices from the Expedia team to manage AppDynamics efficiently and effectively
This deck was originally presented at AppSphere 2015.
#TravTechDisrupt
Presentation by Andreas Nau, Managing Director for Central Europe, Expedia
Andreas manages the Expedia brand across the Central European region and has over nine years of experience in the travel and technology sectors, having worked across TUI AG and TUI Travel PLC.
To celebrate LondonTechnology Week, Travolution once again partnered with Open Destinations and new sponsor VE Interactive to host an event in London’s Tech City.
The event brought together industry leaders and the start-up community to join in a panel discussion to share their views on new entrants in the travel supply chain.
For more info: #TravTechDisrupt
AWS re:Invent 2016: Serverless Computing Patterns at Expedia (SVR306) )Amazon Web Services
In the middle of 2015, Expedia started using AWS Lambda for serverless computing. We built boilerplate templates in Node.js, Java, and Python so development teams could build and deploy serverless applications into AWS. Currently, we have 300 AWS Lambda functions processing 40 million invocations per day.
In this session, we will discuss how development teams use boilerplate templates to create serverless applications with Amazon API Gateway and AWS Lambda and how they deploy them to AWS. We will cover patterns, architectural design choices, and the benefits --- like cost, scale, availability, and operations --- of running serverless applications.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。