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

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

Andreea Visan, Mihai Istin, Florin Pop, Valentin Cristea
Copyright: © 2011 |Pages: 18
DOI: 10.4018/jdst.2011070101
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Abstract

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.
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The Monitoring And State Prediction Tool

This section briefly describes the monitoring and state prediction tool used in our research. Its main design goals were the scalability, the flexibility and the ease-of-use. In the meantime, we assured that the main requirements of a monitoring module are satisfied. Figure 1 presents the architecture of the monitoring and state prediction tool taken into account. Its main components are represented by the monitoring module, the repository server, the prediction server, the database and the web server. These components will be briefly introduced in the current section.

Figure 1.

Architecture of the monitoring and state prediction tool

jdst.2011070101.f01

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