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Due to the proliferation of requests in heterogeneous resources in edge computing, the existence of a large number of tasks and workloads in virtual machines in the edge computing environment is inevitable. Thus, load balancing strives to facilitate an even distribution of workload across available resources. Its purpose is to provide continuous se...
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... On the one hand, energy consumption of the devices at the edge of network may increase by 1.5 times due to the fact that these devices with a high power consumption state need to transmit the large-scale data to data center for centralized processing [3]. On the other hand, load balancing has become a hot topic for joint cloud frameworks of combining edge computing centers with cloud computing centers [4,5]. With the proliferation of requests of the heterogeneous resources in edge computing, there exists a large number of tasks for edge computing. ...
... Edge computing pushes these tasks to a location close to the data source, or even deploys the entire computing to a node on the transmission path from the data source to the cloud computing center. Such computing deployment requires load balancing on the existing network structure to provide continuous service and to ensure fair load distribution among edge computing resources [4]. In sum, reducing network energy consumption and balancing workload are two important optimization goals for data transmission in edge computing field. ...
Reducing network energy consumption and balancing workload are two key optimization goals for data transmission in edge computing field. However, these two goals are likely to be conflicting in some cases and fail to achieve the optimum simultaneously. In this paper, we design a new data transmission optimization algorithm using multi-objective reinforcement learning. We design the vector of rewards for the two objectives, and update Pareto approximate set by multiple state steps to approach the optimal solution. In every step, we classify the candidate links into four different levels for path selection. We aggregate network traffic to construct minimum topology subset, minimizing the number of occupied device to reduce energy consumption. We optimize the load distribution on those selected links, minimizing maximum congestion factor to balance workload. For action selection, we leverage roulette-based Chebyshev scalarization function to solve the weight selection problem for multi-objectives and enforce exploration to avoid falling into local optimum. To improve the convergence rate, we design heuristic factor to control the search of solution space and enhance the guiding effect of the existing optimal solution. Simulation result shows that the proposed algorithm achieves good performance in energy-saving and load balance at the same time.
... Therefore, the use of machine learning in improving routing in VANET can provide an effective solution. The application of machine learning methods has been proven in various fields of science such as wireless networks [3,4], social networks [5], network security [6][7][8], and pattern recognition [9][10][11][12][13][14]. One of the most important challenges in VANET is the delay in sending and receiving messages between vehicles. ...
In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly improved compared to the previous methods in terms of package delivery rate. Also, the proposed method with a total delay of 24.566 ms has a shorter delay than the previous methods in terms of message sending delay in the network.
