Intelligent grid routing and scheduling

Scheduling and routing are two main research areas in the Grid. The overview of algorithms from both research areas shows that only simple (i.e., rather straightforward) algorithms are used for both: problems of routing and scheduling. Moreover, these algorithms are usually static in the sense of their flexibility and extensibility. While an suboptimal, but simple algorithm, with precisely defined input and output is still useful, the real obstacle to real-world use is the users inability to extend it with custom features, required by different uses of the system. Such an algorithm would be far more useful if it supported different plugins that implement different profiles for different users, machines, etc. An additional problem lies in variety of Grid frameworks as each and every one of them supports only its own scheduling and routing algorithm – if the user wants to change it, he has to re-implement it.

This post-doc applied research project will extend the existing approaches with different artificial intelligence (machine learning) methods which will be implemented in the form of plugins and adapt to the GRID environment at hand and, therefore ensure real-world usability.