Model and Operate Datacenter by Kubernetes at eBay 辛肖刚, Cloud Engineering Manager, ebay 梅岑恺, Senior Operation Manager, ebay Agenda About ebay Our fleet Kubernetes makes magic at ebay Model + Controller Controller How we model our datacenter Operation in large scale Q&A About ebay 177M Active buyers worldwide $22.7B Amount of eBay Inc. GMV $2.6B Reported revenue 62% International revenue 1.1B Kubernetes Onboard Provision Configuration Kubernetes You need onboard something from nothing! Let’s model a datacenter running Kubernetes Onboard Provision Configuration Kubernetes After you define your

The Future of Cloud Native Applications with Open Application Model (OAM) and Dapr @markrussinovich Application models Describes the topology of your application and its components The way developers services and data stores Programming models Distributed Application Runtime (Dapr) Open Application Model (OAM) https://oam.dev State of Cloud Native Application Platforms Kubernetes for applications of concerns Application focused Application focused Container infrastructure Open Application Model Service Job Namespace Secret Volume Endpoint ConfigMap VolumeAttach CronJob Deployment

(Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University 2020 Modeling the world as a graph 2 Social networks friend follows The web Actor-movie results for the search term “graph” ??? Vasiliki Kalavri | Boston University 2020 Basics 1 5 4 3 2 “node” or “vertex” “edge” 1 5 4 3 2 undirected graph directed graph 4 ??? Vasiliki Kalavri Kalavri | Boston University 2020 Graph streams Graph streams model interactions as events that update an underlying graph structure 5 Edge events: A purchase, a movie rating, a like on an online post

(and advantages) • Preview of Numpy & PyTorch & Tensorflow Numpy Tensorflow PyTorch Computation Graph Advantages (continued) • Which one do you think is better? Advantages (continued) • Which one (visualise computation graph) • Various other functions • loss (MSE,CE etc..) • optimizers Prepare Input Data •Load data •Iterate over examples Train Model •Train weights Evaluate Model •Visualise …... Model • In PyTorch, a model is represented by a regular Python class that inherits from the Module class. • Two components • __init__(self): it defines the parts that make up the model —in our

poles placement, sampling period, damping Cannot identify individual bottlenecks neither model 2-input operators ??? Vasiliki Kalavri | Boston University 2020 Heuristic models 11 • Metrics University 2020 src o1 o2 10 recs 10 recs 1 2 3 4 100 rec 100 recs Intuition: use the dataflow graph to extract operator dependencies and system instrumentation to collect accurate, representative University 2020 src o1 o2 10 recs 10 recs 1 2 3 4 100 rec 100 recs Intuition: use the dataflow graph to extract operator dependencies and system instrumentation to collect accurate, representative

样本分布不均匀 ✗ 隐私保护 • 多个环节 • 多种模型 ✗ 海量参数 ✗ 海量数据 从FM到DeepFM rt 增 加了10倍怎么优化？ 2.模型效果优 化困难 1.方案复杂 Data Model Compute Platform 要求:  准确: 低噪声  全面: 同分布 模型选型:  容量大  计算量小 训练推理:  高qps, 低rt  支持超大模型  性价比 Tensorflow PyTorch Parameter Server MPI TreeModel SQL MapReduce Blink  场景丰富: 图像/视频/推荐/搜索  大数据+大模型: Model Zoo  跨场景+跨模态  开箱即用: 封装复杂性  白盒化, 可扩展性强  积极对接开源系统+模型 FTRL SGD Adam Solutions Librarys 优势: (user/item/attribute)  动态图 标准化: Standard Libraries Graph-Learn: 分布式图算法库 标准化: Standard Solutions Continuous Optimization: Active learning Data Label Model Serving CV / NLP解决方案: EAS Web App Mobile

interesting aspects of the data Examples: Discovering clusters Discovering latent factor Discovering graph structure Matrix completion Feng Li (SDU) Overview September 6, 2023 28 / 57 Unsupervised Learning: Discovering Graph Structures Sometimes we measure a set of correlated variables, and we would like to discover which ones are most correlated with which others This can be represented by a graph, in which (SDU) Overview September 6, 2023 47 / 57 Parametric vs Non-Parametric Models Parametric model We can train a model by using the training data to estimate parameters of it Use these parameters to make predictions

available for Linux from Octave-Forge ). 2 Linear Regression Recall that the linear regression model is hθ(x) = θT x = n � j=0 θjxj, (1) where θ is the parameter which we need to optimize and x is dataset. There are m = 50 training examples, and you will use them to develop a linear regression model using gradient descent algorithm, based on which, we can predict the height given a new age value plotting your results later. We implement linear regression for this problem. The linear regression model in this case is hθ(x) = θT x = 1 � i=0 θixi = θ1x1 + θ2, (4) (1) Implement gradient descent using

learning algorithms help build models, which as the name suggests is an approximate mathematical model of what outputs correspond to a given input. To illustrate, when you visit Netflix’s homepage, the might be popular with other users too. If we train a model to predict the probability based on your behavior and currently trending content, the model will assign a high probability to Seinfeld. While there the performance of the model scaled well with the number of labeled examples, since the network had a large number of parameters. Thus to extract the most out of the setup, the model needed a large number

single-pass Updates arbitrary append-only Update rates relatively low high, bursty Processing Model query-driven / pull-based data-driven / push-based Queries ad-hoc continuous Latency relatively University 2020 Time-Series Model: The jth update is (j, A[j]) and updates arrive in increasing order of j, i.e. we observe the entries of A by increasing index. This can model time-series data streams: sequence of measurements from a temperature sensor • the volume of NASDAQ stock trades over time This model poses a severe limitation on the stream: updates cannot change past entries in A. 11 Useful in