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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.<\/p>","DOI":"10.4018\/jdst.2011070101","type":"journal-article","created":{"date-parts":[[2011,10,20]],"date-time":"2011-10-20T10:38:27Z","timestamp":1319107107000},"page":"1-18","source":"Crossref","is-referenced-by-count":3,"title":["Bio-Inspired Techniques for Resources State Prediction in Large Scale Distributed Systems"],"prefix":"10.4018","volume":"2","author":[{"given":"Andreea","family":"Visan","sequence":"first","affiliation":[{"name":"University Politehnica of Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihai","family":"Istin","sequence":"additional","affiliation":[{"name":"University Politehnica of Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florin","family":"Pop","sequence":"additional","affiliation":[{"name":"University Politehnica of Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentin","family":"Cristea","sequence":"additional","affiliation":[{"name":"University Politehnica of Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jdst.2011070101-0","doi-asserted-by":"crossref","unstructured":"Araujo, A. 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