With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.
Research on nodes localization in Wireless Sensor Networks (WSN) has been a hot spot in recent years. How to improve the reliability and accuracy of nodes localization is a hard and challenging problem in the area, and is far to be solved satisfactorily. This paper proposes an effective self-adapting localization algorithm in WSN based on optimized RSSI and DV-Distance algorithm. In order to enhance the precision of localization, the presented algorithm introduces an effective method to reduce the error of RSSI-measured distance. The algorithm also uses Small-World-Network theory to help select beacon nodes from localized normal nodes, so as to raise the performance and efficiency. Experimental results show that the algorithm has effectively improved the accuracy, self adaptivity, performance and efficiency of nodes localization in WSN.
Wireless Sensor Networks (WSNs) are becoming more and more speedily and widely used nowadays. While it is usually deployed in the non-controlled environment faced with many kinds of threats. So ensure confidence and cooperation among every pair of interacting nodes is critical, but traditional security measures do little help. The Biologically-Inspired Trust Model (BITM) based on Ant Colony Systems (ACS) aiming at providing trust in WSNs is proposed, also the features and architecture of the simulation system. Simulation experiment results demonstrate that the output average path length, the average deviation etc of the proposed model can meet with the situations and improve the nodes’ cooperation
Cloud computing has been extensively focused by both industry and academia. Resource management and scheduling is a basic and important problem in cloud computing environment. This paper proposes a new and effective cloud resource management model and scheduling algorithm based on fuzzy clustering and Distributed hash Table. By introducing effective theory and technology, the proposed approach can: (1) subtly assign the appropriate resources to the requestors that exactly satisfy its’ needs of resources, while effectively avoid unreasonable scheduling of resources; (2) rapidly and effectively locate the resources that literally satisfy the needs of the resource requestor. Simulation experiments show that the proposed approach works better than similar algorithms.
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