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Comparative Performance Measurement of the Pareto Optimal Combination and Multi-Objective Combination Models for Controller Placement in Software-Defined Networks
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作者 Mission Franklin Constance Izuchukwu Amannah 《Journal of Computer and Communications》 2024年第3期84-100,共17页
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 tw 展开更多
关键词 LATENCY Measurement Metrics Performance POCO moko Architecture PROVISION
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香蕉细菌性枯萎病菌在中国的潜在适生区域 被引量:14
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作者 徐进 陈林 +3 位作者 许景生 张争 张昊 冯洁 《植物保护学报》 CAS CSCD 北大核心 2008年第3期233-238,共6页
青枯菌2号小种引起的香蕉细菌性枯萎病(moko disease)是香蕉生产上最具毁灭性的病害之一。为指导香蕉细菌性枯萎病的预防控制和制定相关的检疫政策,该研究根据EPPO公布的香蕉细菌性枯萎病在全球范围内的分布资料,分别采用GARP和MAXENT... 青枯菌2号小种引起的香蕉细菌性枯萎病(moko disease)是香蕉生产上最具毁灭性的病害之一。为指导香蕉细菌性枯萎病的预防控制和制定相关的检疫政策,该研究根据EPPO公布的香蕉细菌性枯萎病在全球范围内的分布资料,分别采用GARP和MAXENT两种预测模型对其在中国的潜在适生区域进行分析预测。结果显示,GARP和MAXENT的预测结果基本一致,均表明香蕉细菌性枯萎病菌在中国的潜在适生区域集中分布于东南部的云南、广西、广东、海南、福建、台湾、江西、湖南、贵州、四川、重庆、浙江、湖北等13个省(市、自治区),其中高风险适生区域包括广东、广西、台湾、海南、福建和云南省。 展开更多
关键词 香蕉细菌性枯萎病 GARP MAXENT 潜在适生区域
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