The evolution of the current network has challenges of programmability, maintainability and manageability, due to network ossification. This challenge led to the concept of software-defined networking (SDN), to decoup...The evolution of the current network has challenges of programmability, maintainability and manageability, due to network ossification. This challenge led to the concept of software-defined networking (SDN), to decouple the control system from the infrastructure plane caused by ossification. The innovation created a problem with controller placement. That is how to effectively place controllers within a network topology to manage the network of data plane devices from the control plane. The study was designed to empirically evaluate and compare the functionalities of two controller placement algorithms: the POCO and MOCO. The methodology adopted in the study is the explorative and comparative investigation techniques. The study evaluated the performances of the Pareto optimal combination (POCO) and multi-objective combination (MOCO) algorithms in relation to calibrated positions of the controller within a software-defined network. The network environment and measurement metrics were held constant for both the POCO and MOCO models during the evaluation. The strengths and weaknesses of the POCO and MOCO models were justified. The results showed that the latencies of the two algorithms in relation to the GoodNet network are 3100 ms and 2500 ms for POCO and MOCO respectively. In Switch to Controller Average Case latency, the performance gives 2598 ms and 2769 ms for POCO and MOCO respectively. In Worst Case Switch to Controller latency, the performance shows 2776 ms and 2987 ms for POCO and MOCO respectively. The latencies of the two algorithms evaluated in relation to the Savvis network, compared as follows: 2912 ms and 2784 ms for POCO and MOCO respectively in Switch to Controller Average Case latency, 3129 ms and 3017 ms for POCO and MOCO respectively in Worst Case Switch to Controller latency, 2789 ms and 2693 ms for POCO and MOCO respectively in Average Case Controller to Controller latency, and 2873 ms and 2756 ms for POCO and MOCO in Worst Case Switch to Controller latency respectively. The latencies of the two algorithms evaluated in relation to the AARNet, network compared as follows: 2473 ms and 2129 ms for POCO and MOCO respectively, in Switch to Controller Average Case latency, 2198 ms and 2268 ms for POCO and MOCO respectively, in Worst Case Switch to Controller latency, 2598 ms and 2471 ms for POCO and MOCO respectively, in Average Case Controller to Controller latency, 2689 ms and 2814 ms for POCO and MOCO respectively Worst Case Controller to Controller latency. The Average Case and Worst-Case latencies for Switch to Controller and Controller to Controller are minimal, and favourable to the POCO model as against the MOCO model when evaluated in the Goodnet, Savvis, and the Aanet networks. This simply indicates that the POCO model has a speed advantage as against the MOCO model, which appears to be more resilient than the POCO model.展开更多
歌剧作曲家罗西尼是19世纪意大利的音乐天才,仅用十三天完成的歌剧《塞维利亚理发师》是喜歌剧最经典的代表作。这部歌剧的原型出自法国作家博马舍于1775年所写的剧本,原名《Le Barbier de Séville》。故事延续自博马舍的剧本《费...歌剧作曲家罗西尼是19世纪意大利的音乐天才,仅用十三天完成的歌剧《塞维利亚理发师》是喜歌剧最经典的代表作。这部歌剧的原型出自法国作家博马舍于1775年所写的剧本,原名《Le Barbier de Séville》。故事延续自博马舍的剧本《费加罗的婚礼》,而莫扎特于1786年作曲的同名歌剧即是基于此而写的。本文拟对《塞维利亚理发师》这部歌剧和剧中罗西娜的咏叹调《美妙的歌声随风荡漾》(Una voce poco fa{II Barbiere di Siviglia})做个简单的介绍,对罗西尼的创作特点和音乐风格进行粗浅的探究,对这首咏叹调的音乐进行较详细的分析,并谈谈本人演唱这首咏叹调的体会。展开更多
In PCAC(Po?os de Caldas Alkaline Complex),in southeastern Brazil,it is observed a polyphase mineralization related to Zr-,U-,Th-,Mo-,and REE-enrichment due to hydromethermal processes which affected alkaline primary r...In PCAC(Po?os de Caldas Alkaline Complex),in southeastern Brazil,it is observed a polyphase mineralization related to Zr-,U-,Th-,Mo-,and REE-enrichment due to hydromethermal processes which affected alkaline primary rock.Primary Zr-minerals were leached and concentrated as“caldasite”,a rock composed mainly by zircon and baddeleyite in different proportions.Several techniques of mineralogical characterization were applied and results indicated zircon,baddeleyite,magnetite and iron-oxyhydroxides,mainly.Magnetic separation by WHIMS(Wet High-Intensity Magnetic Separation)was performed in order to indicate the efficiency for Fe-concentration removal for potential application in refractory industry.展开更多
The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer...The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.展开更多
文摘The evolution of the current network has challenges of programmability, maintainability and manageability, due to network ossification. This challenge led to the concept of software-defined networking (SDN), to decouple the control system from the infrastructure plane caused by ossification. The innovation created a problem with controller placement. That is how to effectively place controllers within a network topology to manage the network of data plane devices from the control plane. The study was designed to empirically evaluate and compare the functionalities of two controller placement algorithms: the POCO and MOCO. The methodology adopted in the study is the explorative and comparative investigation techniques. The study evaluated the performances of the Pareto optimal combination (POCO) and multi-objective combination (MOCO) algorithms in relation to calibrated positions of the controller within a software-defined network. The network environment and measurement metrics were held constant for both the POCO and MOCO models during the evaluation. The strengths and weaknesses of the POCO and MOCO models were justified. The results showed that the latencies of the two algorithms in relation to the GoodNet network are 3100 ms and 2500 ms for POCO and MOCO respectively. In Switch to Controller Average Case latency, the performance gives 2598 ms and 2769 ms for POCO and MOCO respectively. In Worst Case Switch to Controller latency, the performance shows 2776 ms and 2987 ms for POCO and MOCO respectively. The latencies of the two algorithms evaluated in relation to the Savvis network, compared as follows: 2912 ms and 2784 ms for POCO and MOCO respectively in Switch to Controller Average Case latency, 3129 ms and 3017 ms for POCO and MOCO respectively in Worst Case Switch to Controller latency, 2789 ms and 2693 ms for POCO and MOCO respectively in Average Case Controller to Controller latency, and 2873 ms and 2756 ms for POCO and MOCO in Worst Case Switch to Controller latency respectively. The latencies of the two algorithms evaluated in relation to the AARNet, network compared as follows: 2473 ms and 2129 ms for POCO and MOCO respectively, in Switch to Controller Average Case latency, 2198 ms and 2268 ms for POCO and MOCO respectively, in Worst Case Switch to Controller latency, 2598 ms and 2471 ms for POCO and MOCO respectively, in Average Case Controller to Controller latency, 2689 ms and 2814 ms for POCO and MOCO respectively Worst Case Controller to Controller latency. The Average Case and Worst-Case latencies for Switch to Controller and Controller to Controller are minimal, and favourable to the POCO model as against the MOCO model when evaluated in the Goodnet, Savvis, and the Aanet networks. This simply indicates that the POCO model has a speed advantage as against the MOCO model, which appears to be more resilient than the POCO model.
文摘歌剧作曲家罗西尼是19世纪意大利的音乐天才,仅用十三天完成的歌剧《塞维利亚理发师》是喜歌剧最经典的代表作。这部歌剧的原型出自法国作家博马舍于1775年所写的剧本,原名《Le Barbier de Séville》。故事延续自博马舍的剧本《费加罗的婚礼》,而莫扎特于1786年作曲的同名歌剧即是基于此而写的。本文拟对《塞维利亚理发师》这部歌剧和剧中罗西娜的咏叹调《美妙的歌声随风荡漾》(Una voce poco fa{II Barbiere di Siviglia})做个简单的介绍,对罗西尼的创作特点和音乐风格进行粗浅的探究,对这首咏叹调的音乐进行较详细的分析,并谈谈本人演唱这首咏叹调的体会。
文摘In PCAC(Po?os de Caldas Alkaline Complex),in southeastern Brazil,it is observed a polyphase mineralization related to Zr-,U-,Th-,Mo-,and REE-enrichment due to hydromethermal processes which affected alkaline primary rock.Primary Zr-minerals were leached and concentrated as“caldasite”,a rock composed mainly by zircon and baddeleyite in different proportions.Several techniques of mineralogical characterization were applied and results indicated zircon,baddeleyite,magnetite and iron-oxyhydroxides,mainly.Magnetic separation by WHIMS(Wet High-Intensity Magnetic Separation)was performed in order to indicate the efficiency for Fe-concentration removal for potential application in refractory industry.
文摘The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.
