Gustavo Alonso: Catalogue data in Autumn Semester 2009

Name Prof. Dr. Gustavo Alonso
FieldInformatik
Address
Institut für Computing Platforms
ETH Zürich, STF K 513
Stampfenbachstrasse 114
8092 Zürich
SWITZERLAND
Telephone+41 44 632 73 06
E-mailalonso@inf.ethz.ch
URLhttp://www.inf.ethz.ch/~alonso
DepartmentComputer Science
RelationshipFull Professor

NumberTitleECTSHoursLecturers
251-0307-00LEnterprise Application Integration6 credits2V + 2U + 1AG. Alonso
AbstractThe course concentrates on the implementation of distributed information technology infrastructure as used in enterprises. Topics covered include RPC, RMI, Corba, Middleware architecture, web services, security, replication, transactions and consistency. The course also involves a comprehensive project work where students must solve a complex data integration problem in a realistic setting.
ObjectiveUnderstanding the architecture of modern information systems
ContentThe course will explore modern concepts in IT architecture such as Service Oriented Architectures, and Web services. The lectures will cover the design and architecture of large information systems, such as those found behind commercial web sites, scientific servers, or data clusters. The course aims at providing an in depth review of the evolution and state of the art of the tools and methodologies used to build large information systems. In particular, the role of middleware, databases, programming languages and distributed systems will be discussed in light of the new requirements imposed by the Internet and the large amounts of data involved. The course will emphasize practical aspects and will be organized around concrete examples taken from real applications and commercial products.
Lecture notesAdditonal Course notes and supporting material will be distributed during the lecture
LiteratureG. Alonso, F. Casati, H. Kuno, V. Machiraju:
"Web Services - Concepts, Architectures and Applications"
Springer, 2004 - ISBN 3-540-44008-9
Prerequisites / NoticeCompletion of the project is a requirement for taking the exam.
251-0817-00LDistributed Systems Laboratory Information
In the Master Programme max. 10 credits can be accounted by Labs
on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
10 credits9PF. Mattern, G. Alonso, T. Roscoe, R. Wattenhofer
AbstractThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including wireless networks, ad-hoc networks, RFID, and distributed applications on mobile phones or PDAs.
ObjectiveGain hands-on-experience with real products and the latest technology in distributed systems.
ContentThis course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on PDAs. The goal of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.
For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso.
251-0915-00LDistributed Information Systems Restricted registration - show details 2 credits2SG. Alonso, D. Kossmann
AbstractLatest Topics in the field of Distributed Information Systems will be discussed.
Objective
251-0929-00LMobile Information and Communication Systems Restricted registration - show details 2 credits2SG. Alonso, D. Kossmann, F. Mattern, L. Thiele
AbstractLatest Topics in the field of Mobile Information and Communication Systems will be discussed.
Objective
252-3500-06LInformation and Communication Systems
Does not take place this semester.
2 credits2SG. Alonso, D. Kossmann, T. Roscoe, N. Tatbul Bitim
AbstractThe seminar deals with a current topic in distributed information systems. Students are expected to attend the entire seminar, choose a topic for presentation (may be either a collection of research papers or describing a system and/or evaluating a concrete product). Students are evaluated in the knowledge gained, the presentation made and the report they will present at the end of the semester.
ObjectiveIn this edition (HS 2008): The seminar course will look at new architectures for data processing systems brought about by recent trends in hardware design such as multi-core and parallel processing.
263-0007-00LAdvanced Systems Lab6 credits2V + 2U + 1AD. Kossmann, G. Alonso, T. Roscoe
AbstractThe goal of this course is to teach students how to evaluate the performance of complex computer and software systems. Accordingly, the methodology to carry out experiments and measurements is studied.
Furthermore, the modelling of systems with the help of queueing network systems is explained.
ObjectiveThe goal of this course is to teach students how to evaluate the performance of complex computer and software systems.
263-3000-00LMassively Parallel Data Analysis with MapReduce
Does not take place this semester.
5 credits2V + 2AD. Kossmann, G. Alonso, T. Roscoe, N. Tatbul Bitim
AbstractThe purpose of this course is to teach students how to carry out massively parallel data analysis using MapReduce as the programming abstraction and Hadoop on top of a (large) cluster of machines in order to get hands on experience and solve real problems.
Objective
ContentMany applications involve the processing and analysis of huge amounts of data. Typical examples are Web-scale search engines (such as Google, MSN, or Yahoo), new Web applications such as Flickr or Google Maps, and scientific applications (e.g., in the life sciences or physics). A typical analysis of this data would, for instance, detect certain behavior patterns in a Web log or the detection of star constellations in telescope images.

Given the amounts of data that need to be analyzed, parallelization on large clusters of machines is a must in order to get acceptable response times. The idea is to partition the data into "chunks" and process a large set of chunks in parallel. The first large-scale implementation of this idea on thousands of machines was implemented by Google using the so-called MapReduce paradigm. MapReduce is a programming framework designed for the analysis of masses of data. Its implementation makes use of the Google File System (GFS) which is a distributed file system designed to store peta-bytes of data on thousands of machines.

Recently, Yahoo and the Apache Foundation launched an open-source implementation of MapReduce and a distributed file system. This implemenation is called Hadoop and has been shown to scale up to 2000 machines. Google is establishing a data center for Academic use with 1000 machines that operates using Hadoop. This data center can potentially be used to run programs as part of this course.

The purpose of this course is to teach students how to carry out massively parallel data analysis using MapReduce as the programming abstraction and Hadoop on top of a (large) cluster of machines in order to get hands on experience and solve real problems. The course will have two parts:

a.) Six week of classes in order to understand the underlying technology (distributed file system, scheduling in warehouse-size data centers, and the Sawzall programming language used in the MapReduce framework).
b.) Projects: solving a big data analysis problem (e.g., Web log mining, discovering intelligent life in space, etc.)