An Enhanced Ontology Discovery System for Sustainable Hybrid Cloud Infrastructure Services
| Project/Area Number |
24500100
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Computer system/Network
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| Research Institution | Kyushu Sangyo University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
SHIRATORI Norio 早稲田大学, 国際情報通信研究センター, 教授 (60111316)
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| Research Collaborator |
RAHAYU Wenny J. オーストラリア、ラトローブ大学, 計算機科学部, 教授
TANIAR David オーストラリア、モナシュ大学, ビジネスシステム校, 准教授
UCHIBAYASHI Toshihiro 九州産業大学, 非常勤講師 (90615353)
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| Project Period (FY) |
2012-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | オントロジー / エージェント / クラスタリング / ハイブリッドクラウド / クラウドコンピューティング / 持続的なクラウド / オントロジ / クラウドインフラサービス / 情報科学 / IaaSクラウドサービス / クラウド・コンピューティング / ハイブリッド・クラウド / マルチエージェント / クラウドオントロジ |
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| Outline of Final Research Achievements |
We proposed a framework and system prototype using agents and existing cloud ontology to filter the different public cloud IaaS services representation to obtain the best-fit required IaaS services. The agents measure the network usage of public clouds at regular time interval enabling the user to conduct search and selection with static and dynamic information. We then develop a search system to provide resiliency support for hybrid cloud. SLA ontology was added and evaluated using heuristic search algorithms. We used a generic method to cluster ontologies to extract sub-ontologies which makes the relationship between elements simpler and easy to store in the database. The search time was conducted by traversing the ontology tree, with clustering, and clustering with database. The latter exhibited a much shorter time. An ontology clustering scheme was proposed and exhibited a bias-free cluster compared to some generic methods.
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Report
(5 results)
Research Products
(11 results)
