Experiment 6: K-Means November 27, 2018 1 Description In this exercise, you will use K-means to compress an image by reducing the number of colors it contains. To begin, download data6.zip and unpack RGB values of the 16 colors present in the image. In this exercise, you will use K-means to reduce the color count to k = 16. That is, you will compute 16 colors as the cluster centroids and replace each instead run K-means on the 128×128 image bird small.tiff. Once you have computed the cluster centroids on the small image, you will then use the 16 colors to replace the pixels in the large image. 3 K-means

Lecture 7: K-Means Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) K-Means December 28, 2021 1 / 46 Outline 1 Clustering 2 K-Means Method 3 K-Means Optimization Problem Problem 4 Kernel K-Means 5 Hierarchical Clustering Feng Li (SDU) K-Means December 28, 2021 2 / 46 Clustering Usually an unsupervised learning problem Given: N unlabeled examples {x1, · · · , xN}; no no. of desired partitions K Goal: Group the examples into K “homogeneous” partitions Loosely speaking, it is classification without ground truth labels A good clustering is one that achieves: High within-cluster

Sebastian Toader & Zsolt Varga 2021-Feb-26 Apache Kafka with Istio on K8s 2 • Scalability • Resiliency • Security • Observability • Disaster recovery Production grade Apache Kafka on Kubernetes certificate attached automatically by Istio Proxy sidecar container • Client certificate includes the K8s service account of the Kafka client application • SPIFE:///ns//sa/

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Technology | United States Product and Service K3s Advancing the Tactical Edge with K3s and SUSE RGS 2 www.susergs.com Advancing the Tactical Edge with K3s and SUSE RGS Introducing Booz Allen Hamilton give warfighters the information edge on the battlefield. Capitalizing on open source solutions like K3s, Booz Allen’s SmartEdge solution allows bat- talions to make real-time, data-driven decisions which and increase the probability of mission success. 3 www.susergs.com Advancing the Tactical Edge with K3s and SUSE RGS Working in collaboration with Brandon Gul- la, Tim Nicklas, Chris Nuber and the team

Scaling a Multi-Tenant k8s Cluster in a Telco Pablo Moncada October 28, 2020 About MasMovil group ● 4th telecom company in Spain ● Provides voice and broadband services to +12M customers ● Several engineers ● Reduce costs ● Security issues ● Scalability issues Namespaces +400 Pods +10k Services +3k CPU +2k Mem +5TB Nodes +300 kube-proxy replacement NetworkPolicy logging Multi-cluster

??? Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/16: Skew mitigation ??? Vasiliki Kalavri | uses two hash functions, H1 and H2 and checks the load of the two sampled workers: P(k) = arg mini(Li(t): H1(k)=i ∨ H2(k)=i) • provably reduces load variation exponentially as compared to the single choice

CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
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??? Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/14: Stream processing optimizations ??? Vasiliki Stream SQL, Scala, Python, Rust, Java… ??? Vasiliki Kalavri | Boston University 2020 Logic State <k, v> <#Brexit, 521> <#WorldCup, 480> <#StarWars, 300> <#Brexit> <#Brexit, 521> Stateful operators Emit(key, AsString(result)); MapReduce combiners example: URL access frequency (k2, list(v2)) → list(v2) (k1, v1) → list(k2, v2) map() reduce() 25 ??? Vasiliki Kalavri | Boston University 2020 MapReduce

Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 2/11: Windows and Triggers Vasiliki Kalavri | Boston