Bio-Inspired Techniques for Resources State Prediction in Large Scale Distributed Systems

The state prediction of resources in large scale distributed systems represents an important aspect for resources allocations, systems evaluation, and autonomic control. The paper presents advanced techniques for resources state prediction in Large Scale Distributed Systems, which include techniques based on bio-inspired algorithms like neural network improved with genetic algorithms. The approach adopted in this paper consists of a new fitness function, having prediction error minimization as the main scope. The proposed prediction techniques are based on monitoring data, aggregated in a history database. The experimental scenarios consider the ALICE experiment, active at the CERN institute. Compared with classical predicted algorithms based on average or random methods, the authors obtain an improved prediction error of 73%. This improvement is important for functionalities and performance of resource management systems in large scale distributed systems in the case of remote control ore advance reservation and allocation.