fogcomputing is oriented to the Internet of Things, which integrates network, computing, storage and application capabilities. It is a semi-virtualized distributed service computing paradigm. It extends data, data pr...
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fogcomputing is oriented to the Internet of Things, which integrates network, computing, storage and application capabilities. It is a semi-virtualized distributed service computing paradigm. It extends data, data processing and applications to the edge of the network and provides intelligent services for users nearby. The purpose of this paper is to design a safe, stable and efficient fogcomputing model. On the basis of the structure of fog computing system, the evolution process of fogcomputing nodes is modeled based on BA scale-free network and ER stochastic network model. Then the evolution process of network hybrid model is analyzed. Finally, the evolution model of fog computing system is solved, and a network model with two network characteristics is obtained. Experiments show that the hybrid network model has the advantages of two basic networks.
fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the ta...
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ISBN:
(数字)9781728175683
ISBN:
(纸本)9781728175683
fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, to achieve the objective of delay reduction remains challenging for offloading strategies due to the resource limitation of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of workload distribution among the heterogeneous fog devices. To cope with the situation, this paper proposes a dynamic task offloading (DTO) approach, which is based on the resource states of fog devices to derive the task offloading policy dynamically. Accordingly, a task can be executed by either a single fog or multiple fog devices through parallel computation of subtasks to reduce the task execution delay. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.
In order to solve the problem of network congestion caused by a large number of data requests generated by intelligent vehicles in LTE-V network, a brand-new fog server with fogcomputing function is deployed on both ...
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In order to solve the problem of network congestion caused by a large number of data requests generated by intelligent vehicles in LTE-V network, a brand-new fog server with fogcomputing function is deployed on both the cellular base stations and vehicles, and an LTE-V-fog network is constructed to deal with delay-sensitive service requests in the Internet of vehicles. The weighted total cost combines delay and energy consumption is taken as the optimisation goal. First a reinforcement learning algorithm Q-learning based on Markov decision process is proposed to solve the problem for minimising weighted total cost. Furthermore, this study specifically explains the setting method of three elements for reinforcement learning-state, action and reward in the fog computing system. Then for reducing the scale of problems and improving efficiency, the authors set up a pre-classification process before reinforcement learning to control the possible values of actions. However, considering that as the number of vehicles in system increases, Q-learning method based on recorded Q values may fall into a dimensional disaster. Therefore, the authors propose a deep reinforcement learning method, deep Q-learning network (DQN), which combines deep learning and Q-learning. Experimental results show that the proposed method has advantages.
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