GRASP-based resource re-optimization for effective big data access in federated clouds
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- 作者：Francesco Palmieria ; francesco.palmieri@unina.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Ugo Fioreb ; ugo.fiore@unina.it" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Sergio Ricciardic ; sergio.ricciardi@ac.upc.edu" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Aniello Castiglioned ; castiglione@ieee.org" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Federated clouds ; Big data access ; Resource re-optimization ; Meta-heuristics ; GRASP
- 刊名：Future Generation Computer Systems
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：54
- 期：Complete
- 页码：168-179
- 全文大小：957 K
文摘
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Efficient re-optimization strategy for big data access in multi-tenant cloud infrastructures.
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Based on a Greedy Randomized Adaptive Search meta-heuristic working on a flexible federated cloud model.
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Performs end-to-end path rerouting and Virtual Machine migration to improve access to big data.
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Rebalances cloud resource usage so that more virtual machines can effectively access data sources.