Cloud Computing INFS3208/INFS7208 Re-cap – Lecture 7 • Database Background • Relational Data Bases – Revisit Relational DBs – ACID Properties * – Clustered RDBMs • Non-relational Data Bases – NoSQL concepts – CAP Theorem *** – MongoDB – Cassandra – HBase CRICOS code 00025B 2 Outline • Background of Distributed File Systems – Big […]
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Cloud Computing INFS3208 Re-cap 1. CourseOrientation 2. HistoryandDefinitionofCloudComputing 3. BusinessDriversforcreationofCloudComputing – Capacity Planning, Cost Reduction, Organisational Agility 4. TechnologiesthatimpactCloudComputing – Clustering, Grid Computing, Virtualisation 5. CloudCharacteristics – On-demand usage, Ubiquitous access, Multitenancy, Elastic, Measurable, Resilient. CRICOS code 00025B 2 Outline • CloudDeliveryModels • CloudDeployModels • Cloud-EnablingTechnologies – Broadband Networks and Internet Architecture – Virtualisation Technology
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Cloud Computing INFS3208 Updates • A3 Spec Updated • Individual Project – Presentation (~10 min) + Q&A (3-5 min) • GCP Coupon Survey • Check your balance • Fill in out the survey (see Announcement) if you need one CRICOS code 00025B 2 Cloud Computing INFS3208 Picture: https://www.mygreatlearning.com/blog/apache-spark/ CRICOS code 00025B 3 Cloud Computing INFS3208
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Cloud Computing INFS3208 Updates CRICOS code 00025B 2 Cloud Computing INFS3208 Outline • Apache Spark and its Characteristics • Resilient Distributed Dataset (RDD) • RDD Operations – Transform & Action • Lazy evaluation and RDD Lineage graph • Terms in Spark • Narrow and Wide Dependencies • How Spark works CRICOS code 00025B 3 Cloud
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Copy of hwk4 CS 447 Homework 4 $-$ Dependency Parsing¶ In this homework you will build a neural transition-based dependency parser, based off the paper A Fast and Accurate Dependency Parser using Neural Networks. The setup for a dependency parser is somewhat more sophisticated than tasks like classification or translation. Therfore, this homework contains many
COMS W4167 Computer Animation Fall 2021, Columbia University TR 4:10pm-5:25PM, 1127 . Instructor: (http://www.cs.columbia.edu/~cxz) Important! You will be using Codio (https://codio.com/) for doing and submitting your programminga assignments. In this way, every student will have the same programming environment to work with. We are also going to release start codes and solution executables on the
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ECE391: Computer Systems Engineering Machine Problem 3 Fall 2021 Checkpoint 1: 5:59pm Tuesday 19 October Checkpoint 2: 5:59pm Tuesday 26 October Exam 2: TBD Checkpoint 3: 5:59pm Tuesday 9 November Checkpoint 4: 5:59pm Tuesday 16 November Thanksgiving Fall Break Checkpoint 5: 5:59pm Sunday 5 December Final Demos: TBD Contents 1 Introduction 2 Using the Group
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COMP90051 Statistical Machine Learning Project 2 Description1 (v3 updated 2021-09-19) Due date: 4:00pm Friday, 8th October 2021 Weight: 25%; forming combined hurdle with Proj1 Copyright statement: All the materials of this project—including this specification and code skeleton—are copyright of the University of Melbourne. These documents are licensed for the sole purpose of your assessment in
Recap – Course Structure This course includes 13 lectures and 10 tutorial/practical sessions Lecture 1 Introduction Lecture 2 Adv. topics&appl Lecture 3 Networks & Load Balancing Concepts Orchestration Storage Computation Others Programming and Linux experiences required! Lecture 4 VT: Docker I Lecture 5 VT: Docker II Lecture 6 VT: Docker III Lecture 7 DBs in
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Introduction This is Part C of the lecture for Week 9. The topic for this week is consensus mechanisms, which are an important part of automated decision-making in decentralized and distributed systems. In this lecture, we look at the type of mechanisms used to reach consensus in Blockchain systems, as in the