Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram, and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workloads conditions such that the selected policy has the least expected waiting time for further processing the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.
In conclusion, the traditional centralized CEP architecture limits its developments, because it is hardly robust and scalable due to single point failure or network break. In addition, some applications are geographically distributed which need to detect complex events from the distributed system. Therefore, distributed CEP has been considered recently. @cite_47 devised plan-based CEP across distributed sources. @cite_67 designed an approach for distributed complex event processing for the RFID application. Compared with our work, these methods do not support distributed pattern operators in the CEP systems.
@cite_67 @cite_47
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Radio-frequency identification (RFID) technology has brought tremendous benefits for business processing, especially in incorporating RFID data into supply chain management (SCM). With the explosion of RFID data, there has raised important problem how to mine valuable information from tremendous and potentially infinite volumes of RFID data. Complex event processing (CEP) technology used to process RFID data has attracted increasing attention. However, the current researches usually focus on centralized CEP architecture, which requires greater bandwidth and computational capability, and lacks of robustness and scalability because of single point failure or network break. This paper first proposes a novel distributed CEP architecture, which spreads centralized CEP tasks load over multiple communicating stations by a space-based communication paradigm over a distributed message broker architecture based on Jini network. A distributed complex event detection algorithm based on master-workers pattern is proposed. The results of experiment show that the performance of our approach is remarkable in the large-scale RFID applications. Complex Event Detection (CED) is emerging as a key capability for many monitoring applications such as intrusion detection, sensor-based activity & phenomena tracking, and network monitoring. Existing CED solutions commonly assume centralized availability and processing of all relevant events, and thus incur significant overhead in distributed settings. In this paper, we present and evaluate communication efficient techniques that can efficiently perform CED across distributed event sources. Our techniques are plan-based: we generate multi-step event acquisition and processing plans that leverage temporal relationships among events and event occurrence statistics to minimize event transmission costs, while meeting application-specific latency expectations. We present an optimal but exponential-time dynamic programming algorithm and two polynomial-time heuristic algorithms, as well as their extensions for detecting multiple complex events with common sub-expressions. We characterize the behavior and performance of our solutions via extensive experimentation on synthetic and real-world data sets using our prototype implementation.