High-speed, in-band performance measurement instrumentation for next generation IP networks

Facilitating always-on instrumentation of Internet traffic for the purposes of performance measurement is crucial in order to enable accountability of resource usage and automated network control, management and optimisation. This has proven infeasible to date due to the lack of native measurement mechanisms that can form an integral part of the network‟s main forwarding operation. However, Internet Protocol version 6 (IPv6) specification enables the efficient encoding and processing of optional per-packet information as a native part of the network layer, and this constitutes a strong reason for IPv6 to be adopted as the ubiquitous next generation Internet transport. In this paper we present a very high-speed hardware implementation of in-line measurement, a truly native traffic instrumentation mechanism for the next generation Internet, which facilitates performance measurement of the actual data-carrying traffic at small timescales between two points in the network. This system is designed to operate as part of the routers' fast path and to incur an absolutely minimal impact on the network operation even while instrumenting traffic between the edges of very high capacity links. Our results show that the implementation can be easily accommodated by current FPGA technology, and real Internet traffic traces verify that the overhead incurred by instrumenting every packet over a 10 Gb/s operational backbone link carrying a typical workload is indeed negligible

A Survey on Internet Traffic Measurement and Analysis

As the number of Internet users increasing rapidly in this world, Internet traffic is also increased. In computer network traffic measurement is the process of measuring the amount and type of traffic on a particular network. Internet traffic measurement and analysis are mostly used to characterize and analysis of network usage and user behaviour, but faces the problem of scalability under the explosive growth of Internet traffic and high speed access. It is not easy to handle Tera and Pera-byte traffic data with single server. Scalable Internet traffic measurement and analysis is difficult because a large dataset requires matching commutating and storage resources. To analyse this traffic multiple tools are available. But they do not perform well when the traffic data size increase. As data grows it is necessary to increase the necessary infrastructure to process it. The distributed File System can be used for this purpose, but it has certain limitation such as scalability, availability and fault tolerant. Hadoop is popular parallel processing framework that is widely used for working with large datasets and it is an open source distributed computing platform having MapReduce for distributed processing and HDFS to store huge amount of data. In future work we will present a Hadoop-based traffic monitoring system that perform a multiple types of analysis on large amount of internet traffic in a scalable manner Keywords- Traffic monitoring, Hadoop, MapReduce, HDFS, NetFlow

Internet Traffic Measurement: Trends and Impact to Campus Network

Abstract— University of Lampung (Unila) is an Institution of Higher Education located in Bandar Lampung. Since 2016, Unila has deployed Internet Access Management (IAM) to guarantee the healthiness of the campus network, as well as to enhance the effectiveness of the bandwidth usage. This study focused on internet traffic measurement, conducted in Unila’s campus network during February 1 until February 29, 2016. Overall, this study shows user behavior on their application. The trend data of monthly most popular URL Categories accessed by users was; 1st Computers & Technology with 30032328 hits or 39.1%, the 2nd was Search Engines & Portals with 14214611 hits or 18.5%. There were around 30-40 % of internet traffic was use for Streaming Media activity, it proves that the existence of Streaming Media Activity in Campus Network which contribute to network congestion. During a month doing internet measurement, we identify the most active device/user that are the 1st was Aruba Wireless Controller with total traffic flow 40.45%, the 2nd was CCR-1 with 26.2%, the 3rd was CCR-2 with 16.9%, and the 4th was Digital Library Server with total flow was 0.6%. Monthly uplink traffic total flow was 5889.92 GB while downlink traffic total flow was 61041.35 GB. We made a recommendation to Unila management for implementing traffic provisioning especially on streaming media activity specific on access to Google Global Cache (GGC), to overcome network congestion during peak time period on working hours. Keywords—internet access management, internet traffic measurement; traffic tren

A Survey of Performance Evaluation and Control for Self-Similar Network Traffic

This paper surveys techniques for the recognition and treatment of self-similar network or internetwork traffic. Various researchers have reported traffic measurements that demonstrate considerable burstiness on a range of time scales with properties of self-similarity. Rapid technological development has widened the scope of network and Internet applications and, in turn, increased traffic volume. The exponential growth of the number of servers, as well as the number of users, causes Internet performance to be problematic as a result of the significant impact that long-range dependent traffic has on buffer requirements. Consequently, accurate and reliable measurement, analysis and control of Internet traffic are vital. The most significant techniques for performance evaluation include theoretical analysis, simulation, and empirical study based on measurement. In this research, we discuss existing and recent developments in performance evaluation and control tools used in network traffic engineering