The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network re...
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The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network resources sharing for systems and users, but also simplify the complexity of resource scheduling and management, improve the utilization of basic resources, balance network load and optimize network performance. Based on the application of wireless sensor network, this paper proposes a wireless sensor network architecture based on cloud computing. The WSN hardware resources are mapped into resources in cloud computing through virtualization technology, and the resource allocation strategy of the network architecture is proposed. The experiment evaluates the performance of the resource allocation strategy. The proposed heuristicalgorithm is a distributedalgorithm. The complexity of centralized algorithms is high, distributedalgorithms can handle problems in parallel, and reduce the time required to get a good solution with limited traffic.
This paper studies a distributed heuristic algorithm for the generation and adjustment of the formation of homogeneous multi-rotor UAVs to improve the applicability and flexibility of multi-rotor UAV dense formation. ...
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ISBN:
(纸本)9781728175621
This paper studies a distributed heuristic algorithm for the generation and adjustment of the formation of homogeneous multi-rotor UAVs to improve the applicability and flexibility of multi-rotor UAV dense formation. Each drone makes an independent decision using a multi-agent framework based on the received satellite positioning, communication information, and obstacle avoidance sensor information. For the motion mode of a multi-rotor UAV, the action decision method in a dynamic environment is selected, avoiding complicated problems such as sensor handover and prejudging the moving trajectory. The Hungarian algorithm is used to match the target positions to improve the formation efficiency. The distance between each drone and each target position as the element in the cost matrix can ensure that the path does not cross, which is used for the first match when forming the team;the cost matrix element is set to the square of the distance, which can be used for formation transformation and dynamic adjustment. Through simulation, it is verified that the algorithm with action decision and target position matching can be used for dense formation of homogeneous multi-rotor UAVs and that the process is safe and efficient.
Typical radios in ad hoc networks can support multi-rate transmissions. However, traditional routing protocols do not use this feature well in multi-rate ad hoc networks and therefore, the network performance and reso...
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Typical radios in ad hoc networks can support multi-rate transmissions. However, traditional routing protocols do not use this feature well in multi-rate ad hoc networks and therefore, the network performance and resource utilization are not optimized. Some algorithms have been proposed to take advantage of the multi-rate transmission scheme, but their performance is not optimized either. In this paper, we show that a cross-layer optimization based approach can significantly improve the performance of multi-rate ad hoc networks over existing routing algorithms. For this, we consider link interference and propose joint routing and flow rate optimization for optimal performance in multi-rate ad hoc networks, i.e., a Cross-layer Optimization based Model for Multi-rate Ad hoc Networks (COMMAN). Considering the characteristics of multi-rate ad hoc networks, we design and implement a distributedheuristic of this centralized model. It is shown that the distributed heuristic algorithm can approximate the performance of COMMAN closely. (c) 2007 Elsevier B.V. All rights reserved.
Increasingly, many bandwidth-intensive applications have been ported to the cloud platform. In practice, however, some disadvantages including equipment failures, bandwidth overload and long-distance transmission ofte...
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Increasingly, many bandwidth-intensive applications have been ported to the cloud platform. In practice, however, some disadvantages including equipment failures, bandwidth overload and long-distance transmission often damage the QoS about data availability, bandwidth provision and access locality respectively. While some recent solutions have been proposed to cope with one or two of disadvantages, but not all. Moreover, as the number of data objects scales, most of the current offline algorithms solving a constraint optimization problem suffer from low computational efficiency. To overcome these problems, in this paper we propose an approach that aims to make fully efficient use of the cloud resources to enable bandwidth-intensive applications to achieve the desirable level of SLA-specified QoS mentioned above cost-effectively and timely. First we devise a constraint-based model that describes the relationship among data object placement, user cells bandwidth allocation, operating costs and QoS constraints. Second, we use the distributed heuristic algorithm, called DREAM-L, that solves the model and produces a budget solution to meet SLA-specified QoS. Third, we propose an object-grouping technique that is integrated into DREAM-L, called DREAM-LG, to significantly improve the computational efficiency of our algorithm. The results of hundreds of thousands of simulation-based experiments demonstrate that DREAM-LG provides much better data availability, bandwidth provision and access locality than the state-of-the-art solutions at modest cloud operating costs and within a small and acceptable range of time.
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