Handle Edge Cloud Network with KubeBus Yulin Sun, yulin.sun@huawei.com Seattle Cloud Lab, Huawei R&D USA, Bellevue WA Agenda • Edge sample user scenarios • Edge network characteristics • Related work Sample Scenarios HiLens Campus surveillance Huawei Hilens Edge network characteristics • Edge Nodes running at private network • Connect to Cloud behind NAT gateway • Mightn’t have direct connection Cluster Management • There is cloud cluster, edge cluster, i.e. multiple nodes running in private network • Edge nodes, Edge cluster and cloud cluster needs acting as a single cluster Edge Node Management

Handle Edge Cloud Network with KubeBus Yulin Sun, yulin.sun@huawei.com Seattle Cloud Lab, Huawei R&D USA, Bellevue WA Agenda • Edge sample user scenarios • Edge network characteristics • Related work Sample Scenarios HiLens Campus surveillance Huawei Hilens Edge network characteristics • Edge Nodes running at private network • Connect to Cloud behind NAT gateway • Mightn’t have direct connection Cluster Management • There is cloud cluster, edge cluster, i.e. multiple nodes running in private network • Edge nodes, Edge cluster and cloud cluster needs acting as a single cluster Edge Node Management

Train-Val-Test划分 主讲人：龙良曲 Recap How to detect Splitting Train Set Test Set For example 60K 10K test while train train test trade-off Overfitt ing For others judge ▪ Kaggle Train Set Test Set Set Val Set Unavailable train-val-test K-fold cross-validation Train Set Test Set Val Set k-fold cross validation ▪ merge train/val sets ▪ randomly sample 1/k as val set 下一课时 减轻Overfitting Thank

prepared for professionals who are aspiring to make a career in the field of deep learning and neural network framework. This tutorial is intended to make you comfortable in getting started with the Keras framework 12 Convolutional Neural Network (CNN) ........................................................................................................... 13 Recurrent Neural Network (RNN) .................. ........................................................... 71 12. Keras ― Convolution Neural Network ................................................................................................

网 网络 络 OPERATOR 概述 概述 4.1. CLUSTER NETWORK OPERATOR 4.2. DNS OPERATOR 4.3. INGRESS OPERATOR 4.4. 外部 DNS OPERATOR 4.5. INGRESS NODE FIREWALL OPERATOR 4.6. NETWORK OBSERVABILITY OPERATOR 第 第 5 章 章 OPENSHIFT PLATFORM 中的 中的 CLUSTER NETWORK OPERATOR 5.1. CLUSTER NETWORK OPERATOR 5.2. 查看集群网络配置 5.3. 查看 CLUSTER NETWORK OPERATOR 状态 5.4. 查看 CLUSTER NETWORK OPERATOR 日志 5.5. CLUSTER NETWORK OPERATOR 配置 5.6. 其他资源 VRF 分配从属网络 第 第 26 章 章 硬件网 硬件网络 络 26.1. 关于单根 I/O 虚拟化（SR-IOV）硬件网络 26.2. 安装 SR-IOV NETWORK OPERATOR 26.3. 配置 SR-IOV NETWORK OPERATOR 26.4. 配置 SR-IOV 网络设备 26.5. 配置 SR-IOV 以太网网络附加 26.6. 配置 SR-IOV INFINIBAND

章 分类问题 2 集共 70000 张图片。其中 60000 张图片作为训练集?train(Training Set)，用来训练模型，剩 下 10000 张图片作为测试集?test(Test Set)，用来预测或者测试，训练集和测试集共同组成 了整个 MNIST 数据集。 考虑到手写数字图片包含的信息比较简单，每张图片均被缩放到28 × 28的大小，同时 只保留了灰度信息，如图 ? (?) − ?? (?)) 2 10 ?=1 ? ?=1 只需要采用梯度下降算法来优化损失函数得到?和?的最优解，然后再利用求得的模型去 预测未知的手写数字图片? ∈ ?test即可。 3.4 真的解决了吗 按照上述方案，手写数字图片识别问题似乎得到较好地解决？事实果真如此吗？深入 研究的话，就会发现，至少存在两大问题： 预览版202112 第 3 章 分类问题 模块的输出??连同它的网络层参数??和??等称为一层网络层。特别地，对于网络中间的 层，也叫作隐藏层，最后一层也叫作输出层。这种由大量神经元模型连接形成的网络结构 称为神经网络(Neural Network)。从这里可以看到，神经网络并不难理解，神经网络每层的 节点数和神经网络的层数或结构等决定了神经网络的复杂度。 预览版202112 第 3 章 分类问题 10 输入层：?

to achieve the desired model quality on our task. 2. Fine-tuning: This step adapts a pre-trained network to a specific task. Fine-tuning is compute efficient since we reuse the same base model for all the They demonstrate that using a network pre-trained in this fashion improves the quality of the final object detection task, as compared to randomly initializing the network. Similarly, another task is to 6-4 (b)). The authors report that the network trained in a self-supervised manner this way can be fine-tuned to perform nearly as well as a fully supervised network. 3 Gidaris, Spyros, et al. "Unsupervised

overall architecture review which extrapolated areas of focus for subsequent phases of the assessment. A test plan was created which matched areas of code with specific security controls (e.g. service discovery NCC Group used various hosting options (i.e. Minikube, GKE, KOPS) to build reference clusters and test various configurations. These reference architectures were used to provide testers with a way of validating Method Code-assisted Platforms Golang, Kubernetes Dates 2020-07-06 to 2020-07-31 Environment Local Test Environment Consultants 4 Level of Effort 50 person days Targets istio/istio Istio Source code

Cure53, Dr.-Ing. M. Heiderich, M. Wege, MSc. R. Peraglie, J. Larsson Index Introduction Scope Test Coverage Identified Vulnerabilities DAP-01-002 WP2: Insufficient context separation leads to RCE application (Medium) Miscellaneous Issues DAP-01-001 WP1: Sidecar allows MDNS probes to docker network (Info) DAP-01-007 WP2: HTTP Parameter Pollution in Azure SignalR binding (Info) DAP-01-009 WP2: DAP-01-011 WP2: HTTP Parameter Pollution in Hashicorp secret vault (Low) Orchestration Hardening Network Policy Zero-Trust Concepts RBAC Secrets Management Conclusions Cure53, Berlin · 07/01/20

weight of that connection). Can we do the same with neural networks? Can we optimally prune the network connections, remove extraneous nodes, etc. while retaining the model’s performance? In this chapter depicts two networks. The one on the left is the original network and the one on the right is its pruned version. Note that the pruned network has fewer nodes and some retained nodes have fewer connections Figure 5-1: An illustration of pruning weights (connections) and neurons (nodes) in a neural network consisting of fully connected layers. Exercise: Sparsity improves compression Let's import the