Fast and memory-efficient regular expression matching for deep packet inspection
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
A scalable multithreaded L7-filter design for multi-core servers
Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
Low-Overhead Fault Tolerance for High-Throughput Data Processing Systems
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
Active Replication at (Almost) No Cost
SRDS '11 Proceedings of the 2011 IEEE 30th International Symposium on Reliable Distributed Systems
Scalable and Low-Latency Data Processing with Stream MapReduce
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Fault-tolerant complex event processing using customizable state machine-based operators
Proceedings of the 15th International Conference on Extending Database Technology
Issues and future directions in traffic classification
IEEE Network: The Magazine of Global Internetworking
Deep packet inspection tools and techniques in commodity platforms: Challenges and trends
Journal of Network and Computer Applications
Toward scalable internet traffic measurement and analysis with Hadoop
ACM SIGCOMM Computer Communication Review
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Internet traffic has continued to grow at a spectacular rate over the past ten years. Understanding and managing network traffic have become an important issue for network operators to meet service-level agreements with their customers. In addition, the emergence of high-speed networks, such as 20 Gbps, 40Gbps Ethernet and beyond, requires fast analysis of a large volume of network traffic and this is beyond the capabilities of a single machine. Distributed parallel processing schemes have recently been developed to analyze high quantities of traffic data. However, scalable Internet traffic analysis in real-time is difficult because of a large dataset requires high processing intensity. In this paper, we describe a real-time Deep Packet Inspection (DPI) system based on the MapReduce programming model. We combine a stand-alone classification engine (L7-filter) with the distributed programming MapReduce model. Our experimental results show that the MapReduce programming paradigm is a useful approach for building highly scalable real-time network traffic processing systems. We generate 20 Gbps network traffic to validate the real-time analysis ability of the proposed system.