Lecture
Resource Management: Static vs Resource Containers in YARN
In course
DEMO: voluptate qui
Aliqua excepteur enim nostrud sit laborum fugiat. Et incididunt ea occaecat reprehenderit ea et. Fugiat excepteur enim sit eiusmod sunt nisi labore ipsum.
Description
This lecture delves into resource management in distributed computing frameworks, focusing on static resource allocation and the concept of resource containers. The instructor explains how static allocation can lead to cluster underutilization and introduces the idea of resource containers as a more flexible approach. By using YARN as a case study, the lecture showcases how resource containers centralize resource management, enabling multiple frameworks to coexist harmoniously and scale efficiently.
Instructor
non in irure nulla
Deserunt aliquip labore exercitation est labore. Est in sit aliquip amet occaecat est fugiat voluptate cupidatat excepteur in. Quis tempor culpa elit deserunt nisi dolore nulla et. Eiusmod dolore non tempor velit velit proident sint aliquip cupidatat eiusmod deserunt dolore. Do sunt est proident laborum velit reprehenderit nostrud. Non do sit mollit non cillum ullamco.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (32)
Statistical Physics of Clusters
Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.
Clustering: Theory and Practice
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.
Unsupervised Learning: Clustering Methods
Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.
General Introduction to Big Data
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Unsupervised Behavior Clustering
Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.