Failure of one or multiple critical nodes may partition wireless sensor networks into disjoint segments, and thus brings negative effect on the applications. We propose DCRS, a Distributed Connectivity Restoration Str...Failure of one or multiple critical nodes may partition wireless sensor networks into disjoint segments, and thus brings negative effect on the applications. We propose DCRS, a Distributed Connectivity Restoration Strategy to tolerate the failure of one critical node. Because of the energy restriction of sensor nodes, the energy overhead of the recovery process should be minimized to extend the lifetime of the network. To achieve it, we first design a novel algorithm to identify 2-critical nodes only relying on the positional information of 1-hop neighbors and some 2-hop neighbors, and then we present the criteria to select an appropriate backup for each critical node. Finally, we improve the cascaded node movement algorithm by determining whether a node can move to another non-adjacent node directly or not to reduce the number of nodes moved. The effectiveness of DCRS is validated through extensive simulation experiments.展开更多
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit...In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.展开更多
文摘Failure of one or multiple critical nodes may partition wireless sensor networks into disjoint segments, and thus brings negative effect on the applications. We propose DCRS, a Distributed Connectivity Restoration Strategy to tolerate the failure of one critical node. Because of the energy restriction of sensor nodes, the energy overhead of the recovery process should be minimized to extend the lifetime of the network. To achieve it, we first design a novel algorithm to identify 2-critical nodes only relying on the positional information of 1-hop neighbors and some 2-hop neighbors, and then we present the criteria to select an appropriate backup for each critical node. Finally, we improve the cascaded node movement algorithm by determining whether a node can move to another non-adjacent node directly or not to reduce the number of nodes moved. The effectiveness of DCRS is validated through extensive simulation experiments.
基金Supported by the National High Technology Research and Development Programme of China （ No. 2007AA01Z401 ） and the National Natural Science Foundation of China （No. 90718003, 60973027）.
文摘In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.
