Distributed, heterogeneous resource management using artificial immune systems

As high performance and distributed computing become more important tools for enabling scientists and engineers to solve large computational problems, the need for methods to fairly and efficiently schedule tasks across multiple, possibly geographically distributed, computing resources becomes more crucial. Given the nature of distributed systems and the immense numbers of resources to be managed in distributed and large-scale cluster environments, traditional centralized schedulers will not be extremely effective at providing timely scheduling information. In order to manage large numbers of resources quickly, less computationally intensive methods for scheduling tasks must be explored. This paper proposes a novel resource management system based on the immune system metaphor, making use of the concepts in Immune Network Theory and Danger Theory. By emulating various elements in the immune system, the proposed manager could efficiently execute tasks on very large systems of heterogeneous resources across geographic and/or administrative domains. The distributed nature of the immune system is also exploited in order to allow efficient scheduling of tasks, even in extremely large environments, without the use of a centralized or hierarchical scheduler.