Distributed Computing Rik Sarkar

Distributed Computing Old style: Use a computer for computation

Distributed Computing New style: Use many computers for computation

Examples: Computing clusters (data analysis and storage, hadoop etc) Networks of sensors Mobile systems

Why Study Distributed Computing? Important Applications – Networks, Internet, power grids, social networks, data mining…. Interesting Ideas – Algorithms, graphs, probability, geometry, machine learning…

The computers know only the edges, they can’t see the graph Example: large network analysis

The computers know only the edges, they can’t see the graph Problem: find the real shape of the graph. Example: large network analysis

Spectral Analysis Algorithm

Network from the graph Find the structure behind a network. Use that to design better algorithms

Network from the graph Many uses: Routing in networks. What are the important links? What are the important web pages? (distributed spectral analysis is Google’s secret)

Social Networks Who are influential people or groups? What are the important topics? What causes communities to form? How do epidemics spread?

Mobile networks Can we analyze the location data of people? How do they move? Where do they go?

Computation in Sensor Networks Process the sensor data Find sums, averages, max, min, distribution etc. Do computations using the sensors themselves Use distributed algorithms Has to be very efficient, very fast

Summary: Distributed Computing Important applications in many areas – Large scale data analysis, Sensing and understanding the environment, Energy systems, Mobile systems, robotics… Interesting (fun) techniques – Algorithms, Graph Theory, Learning, Signal processing…

Distributed Computing Rik Sarkar