Course: B534 Distributed systems Graduate/Undergraduate Class

Project Details

Project Lead
Judy Qiu 
Project Manager
Tak-Lon Wu 
Project Members
, Ratul Bhawal, Naveed Alam, Praveen Aravapalli, Kaushik Chandrasekaran, Anesu Chaora, Divya Dwarakanath, Sumit Goyal, Kamlesh Jain, Shivaraman Janakiraman, Harshad Joshi, Swapnil Joshi, DongInn Kim, Naga Malae, Amit Mhatre, Vaibhav Nachankar, Kavin Kumar Palanisamy, Yifan Pan, Ikhyun Park, Adithya Raghavan, Aparna Tiwari, Magesh khanna Vadivelu, Jiang Wu, Haomin Xiang, Tian Xu, Mengchen Yu, Greg Patterson, Matt Sacks, Jonathan Stout, Brennon York, Jared Evans, Manish Kantamneni, Abhijeet Kodgire, Venkata Shravan Ponnam, Vaibhav Shankar, Venkata Raviteja Vutukuri, Erkang You, Rochad Tlusty, Rohith Goparaju, Swathi Gurram, Anand Hegde, Santhosh Kumar Saminathan, Priyank Shah, Arvind Dwarakanath, Dhairya Gala, Vinod Periasamy, Jerome Mitchell, Abhinav Gopisetty, Sankarbala Manoharan, Maitrey Soparia, Sumayah Alrwais, Vasumathi Sridharan, Xiuwen Yang, Hemanth Gokavarapu, Ankur Goyal, Pradnya Kakodkar, Prerna Shraff, Tian Xu, Nabeel Akheel  
Supporting Experts
Tak-Lon Wu,  
Indiana University, School of Informatics and Computing  
Computer Science (401) 


A class of 60 students (PhD, Masters, Undergraduates) covering core Computer Science distributed system curricula.

Intellectual Merit

Class Information Time: MW 2:30PM - 3:45PM Place: Informatics East (I2) 150 Bloomington, IN 47405 Office Hours Instructor: Prof. Judy Qiu AIs: Lkhyun Park, Pairoj Rattadilok Friday 3:00pm to 4:00pm Lindley Hall Room 201D Prerequisites CSCI-P 436 or P536 is required (or permission from the instructor). General programming experience with Windows or Linux using Java, C#, or C++, scripts is preferred. Parallel and cluster computing background is a plus although not required. Objectives The Internet has greatly expanded the scope and importance of distributed systems to include Web 2.0 sites, Information retrieval (search), Utility (cloud) computing, P2P systems and the Internet of things. Further science is facing an unprecedented data deluge and the emergence of data oriented analysis as a fourth paradigm of scientific methodology after theory, experiment and simulation. This class will use these modern systems to introduce core technologies including communication, concurrency/parallelism, security, fault tolerance and programming models. In particular the course will cover programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms and have the opportunity to understand some commercial cloud systems through projects using FutureGrid resources. Scope and topcis The content of B534 will cover the design principles, systems architecture, and innovative applications of parallel, distributed, and cloud computing systems. These include massively parallel processors (MPP), supercomputing clusters, service-orient architecture (SOA), computational grids, P2P (peer-to-peer) networks, virtualized datacenters, cloud platforms, Internet of Things (IOT), and Cyber-Physical Systems (CPS). We will cover MapReduce (originated from functional language) and associated languages like Sawzall and DryadLINQ but it will focus more on the principles and practice of building distributed systems than on languages.

Broader Impacts

The curricula and tutorials can be re-used in other cloud computing/distributed system educational activities

Scale of Use

Each student will need modest resources