1. Distributed Graph Algorithms
Colorado State University
Distributed Graph Algorithms
Wade Lawrence, Roger Marquez, Michael Miller
CS 455
Introduction to Distributed Systems
Dr. Shrideep Pallickara

2. Why is this problem important?
Large graphs common in real-world applications
Social networks
Navigation
We want to be able to run calculations on these graphs
Graphs can have hundreds of millions of vertices and edges
Many graph problems are NP hard

3. Problem Characterization
Execution environment
Optimizing for:
Storage, e.g. state of system
Processor utilization, e.g. message passing
In the context of:
Data centers (renting machines)
Wireless sensors (battery lifetime)

4. Trade-off Space
Distribution of algorithms across a system:
time consuming.
needs to be repeated for every algorithm.
Can use Frameworks:
Simplified distribution.
Imperfect.
Many challenges introduced
In DHTs:
Local state vs lookup time
Routing payloads:
Shortest topological path
Least congested path
Some Examples of tradeoffs that occur in systems that implement DGAs.

5. Dominant Approaches Part 1
Frameworks:
Hadoop, LightGraph, Pregel, and many more.
Provide ability to distribute algorithms with ease
PowerGraph
High concurrency using distributed locking
Good partitioning of power law graphs
LightGraph
Improved PowerGraph
Less internode communication
Better partitioning

6. Dominant Approaches Part 2
Algorithms:
No dominant approach
Depends on intended application
Gallager, Humblet, Spira algorithm
Algorithm developed to find Min. Spanning Tree
Many algorithms built off of this algorithm
New algorithms target weakness of intended system.
Example:
Some algorithms for a battery based system limit the number of messages being passed.
Increases the longevity of the system.

7. Insights Gleaned No blanket solution:
Many unique challenges for every system
Many algorithms exist to produce results
Not many handle specific challenges.
Need to be very careful with choices of algorithm or framework
Frameworks
Provide benefits at costs
No simplification is free
Algorithms
Choice dependent on system.
Must be careful regarding this choice.

8. Future Problem Space Advances in technology
Remove many of previous limitations
Example:
batteries are more powerful, last longer
Processors have more computation power for similar energy costs as today.
Not as much focus on what the system is limited to
Focused on what it should do.
Advancing past limitations fixes some problems
However, it creates many more
Too many choices complicates things

9. Future Trade-offs and Solutions
Improved Networking
Focus on faster, more reliable communication as well as reduced need for communication
Battery life
Not as large a concern as it was in the past
Reducing importance of individual machines
Distributed settings increase processing power rather than hardware improvements