The real-time time-series data of multiple water parameters are acquired via the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of pollution events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively evaluate the abnormal event detection to reduce the detection deviation, a Temproal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, it could analyze the time-series data of multiple parameters with BP (Back Propagation) model to determine the possible outliers, respectively. M-TAEDA algorithm could detect the potential pollution events through Bayesian sequential analysis to estimate the probability of an abnormal event. Finally, it can make decision through the multiple event probability fusion in the water supply systems. The experimental results indicate that the proposed M-TAEDA algorithm can get the 90% accuracy with BP model and improve the rate of detection about 40% and reduce the false alarm rate about 45% compared with the temporal abnormal event detection of Single-Variate Temproal Abnormal Event Detection Algorithm (S-TAEDA).

When the network abnormal event occurs, the spatial-temporal correlation of the sensor nodes is very obvious. While existing methods generally separate time and space data properties, a decentralized algorithm of spatial-temporal abnormal detection based on Probabilistic Graphical Model (PGM) was proposed. Firstly the Connected Dominating Set (CDS) algorithm was used to select part of the sensor nodes to avoid monitoring all the sensor nodes, and then Markov Chain (MC) was used to predict time exception event, at last Bayesian Network (BN) was utilized in modelling the spatial dependency of sensors, combining spatio-temporal events to predict whether the abnormal events would or would not occur. Compared with the simple threshold algorithm and BN algorithm, the experimental results demonstrate that the proposed algorithm has higher detection precision, and low delay rate, greatly reducing the communication overhead and improving the response speed.

Concerning the delay of related task scheduling in cloud computing, a Related Task Scheduling algorithm based on Task Hierarchy and Time Constraint (RTS-THTC) was proposed. The related tasks and task execution order were represented by Directed Acyclic Graph (DAG), and the task execution concurrency was improved by using the proposed hierarchical task model. Through the calculation of the total time constraint in each task layer, the tasks were dispatched to the resource with the minimum execution time. The experimental results demonstrate that the proposed RTS-THTC algorithm can achieve better performance than Heterogeneous Earliest-Finish-Time (HEFT) algorithm in the terms of the total execution time and task delay.