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RISS 인기검색어
- 검색키워드
- 저자 : Jaiganesh, S. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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SOV2C2 : Secure Orthogonal View of Virtualization in Cloud Computing
Jaiganesh M.,Vincent Antony Kumar 한국산학기술학회 2012 SmartCR Vol.2 No.4
Cloud computing is starting a new era in getting information through any Internet connection via any connected device. It provides a pay by use method. The services required by the clients are offered on-demand. Cloud service providers treat the client as a virtual client to accommodate the virtual environment of cloud computing. The major issue in cloud computing is providing security against the unauthenticated accessibility of cloud services. We propose a prototype called secure orthogonal view to handle the security issues occurring at the hypervisor level and at the datacenter level. The attacker may enter the cloud through a hypervisor zone or datacenter zone and cause threats to the data of legitimate users. It is based on a virtual management perception called a Cloud Administrator that handles the security at two levels called virtual hypervisor security (VHS) and Datacenter Security (DCS). We analyzed and discussed factors and their issues to secure a view’s solutions by classifying VHS and DCS using a KVM emulator. These secure view scenarios are prepared to overcome the current cloud computing security issues and have become a strong base for next-generation secure systems.
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Optimization of Drilling Characteristics for AI/SiC_p Composites Using Fuzzy/GA
Karthikeyan, R.,Jaiganesh, S.,Pai, B. C. 대한금속학회 2002 METALS AND MATERIALS International Vol.8 No.2
In this paper an attempt has been made to optimize the drilling characteristics for Al/SiCp composites using fuzzy logic and genetic algorithms (GA). The drilling characteristics studied were drill wear, specific energy and surface roughness. The parameters considered for the study include volume fraction of SiC in the aluminium matrix, cutting speed and feed rate. The experimental data was trained and simulated using fuzzy logic and optimization of cutting conditions were performed using genetic algorithms. The optimized cutting conditions were validated using confirmation experiments.
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