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Scheduling of fog networks with optimized knapsack by symbiotic organisms search

机译:通过共生生物搜索优化背包的雾网络调度。

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Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms.
机译:物联网的概念是使用无线传感器网络,该传感器网络在处理数据并将数据发送到云时会限制功率,存储和延迟。雾计算作为云服务到网络边缘的扩展,可减少延迟和流量,因此在医疗保健,可穿戴设备,智能交通系统和智能城市中非常有用。调度是雾计算中的NP难题。由于靠近传感器和云而导致的边缘设备具有处理能力,并且对于资源管理算法是有益的。我们提出了一种基于背包的计划,该计划通过共生生物搜索进行了优化,该计划在iFogsim中作为雾计算的标准模拟器进行了模拟。结果表明,在我们的调度方法中,能耗降低了18%,总网络使用量降低了1.17%,执行成本降低了15%,传感器寿命降低了5%,优于FCFS(先到先得)和背包算法。

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