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<dc:title xml:lang="pl"><![CDATA[Using a vision cognitive algorithm to schedule virtual machines]]></dc:title>
<dc:creator><![CDATA[Zhao, Jiaqi]]></dc:creator>
<dc:creator><![CDATA[Mhedheb, Yousri]]></dc:creator>
<dc:creator><![CDATA[Tao, Jie]]></dc:creator>
<dc:creator><![CDATA[Jrad, Foued]]></dc:creator>
<dc:creator><![CDATA[Liu, Qinghuai]]></dc:creator>
<dc:creator><![CDATA[Streit, Achim]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[cloud computing]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[vision cognitive algorithm]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[VM scheduling]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[simulation]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NP-hard global optimization problem with grand challenges for researchers.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Abaev, Pavel - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Razumchik, Rostislav - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kołodziej, Joanna - ed.]]></dc:contributor>
<dc:date><![CDATA[2014]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/78963/AMCS_2014_24_3_6.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/88764/edition/78963/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:78963]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 24, number 3 (2014)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=58]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:88764]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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