1. Decentralized Distributed Processing
Michael Tao

2. Abstract Increasing efficiency Power of independent processing limited
Distributed Processing
By decentralizing, anyone can join the network to aid others

3. Background
Projects with large magnitude of data to analyze such as Seti already use applications such as Boinc
Currently there is no way for 3rd parties to join this network without publicizing itself and having each node manually add the project to the list

4. Purpose Communicate and inject jobs to peers in queues
Increasing efficiency of data analysis with the aid of multiple computers which aren't necessarily available

5. Architecture
Local listener for new tasks waits to release work to the network to accomplish some task
Nodes on the network which receive the new work will initialize clients, with variable number of clients at any one time

6. Developments Computer chatting / file transfers Computer load deciding
Modular analysis tool

7. Results Chatting between two computers
Individual clients can now do tasks given to them passed from the main upon initialization