Injecting Robustness into Autonomic Grid Systems

Autonomic computational grids are self-organizing software systems that pool the computational resources of large public networks to solve computationally-intensive problems. While autonomic grids can scale to networks far larger than centralized grids, they have not seen the same adoption and success in industry due to an incomplete treatment of fault tolerance. In this paper, we propose two complementary mechanisms, progressive redundancy and redundant distribution, that secure autonomic grids by injecting robustness into insecure, possibly malicious networks. Progressive redundancy replicates computation in a way that reduces the probability of failure while minimizing the associated cost. Redundant distribution allows for the replication of computation in decentralized networks. We formally define the class of grid technologies to which progressive redundancy and redundant distribution apply and evaluate the cost and benefit of using the techniques. Progressive redundancy and redundant distribution reduce the probability of system failure exponentially, at a linear cost in the execution speed of the system.