On Retrieval Order of Statistics Information from OpenFlow Switches to Locate Lossy Links by Network Tomographic Refinement

To maintain service quality and availability in managed networks, detecting and locating high loss-rate links (i.e., lossy links that are likely congested or physically unstable) in a fast and light-weight manner is required. In our previous study, we proposed a framework of network-assisted location of lossy links on OpenFlow networks. In the framework, a measurement host launches a series of multicast probe packets traversing all full-duplex links; and then the controller retrieves statistics on the arrival of those probe packets at different input ports on different switches and compares them to locate high loss-rate links. The number of accesses to switches required to locate all lossy links strongly depends on the retrieval order in collecting the statistics and should be small as much as possible. Therefore, in this paper, to minimize the necessary number of accesses, we develop a new location scheme with an appropriate retrieval order using a Bayesian-based network tomography to refine candidates for lossy links. The results of numerical simulation on a real-world topology demonstrate the effectiveness of the new location scheme.11th International Conference on Intelligent Networking and Collaborative Systems(INCoS 2019), September 5-7, 2019, Oita, Japa

Dynamic optimization of multicast active probing path to locate lossy links for OpenFlow networks

To maintain a high quality of service in managed networks, detecting and locating high loss-rate links (i.e., lossy links that are likely congested or physically unstable) in a fast and efficient manner is required. In our previous work, we proposed a centrally-managed network-assisted framework of locating lossy links on OpenFlow networks. In the framework, the OpenFlow controller builds a multicast measurement route; a measurement host launches a series of multicast probe packets traversing all full-duplex links along the measurement route; and then the controller collects statistical information (flow-stats) on the arrival of those probe packets at different input ports on selected switches and compares them to narrow down and identify the locations of high loss-rate links. The number of accesses to switches in collecting the flow-stats until locating all lossy links should be as small as possible for fast and efficient measurement. However, it strongly depends on not only the collection order of the flow-stats but also the topological locations of lossy links in the multicast measurement route; the former one was investigated in the previous work but the latter has not been well explored. Therefore, in this paper, we develop a new dynamic scheme of building the multicast measurement route and controlling the collection order of flow-stats from switches, which leverages lossy link locations obtained in the recent past measurements in a repeated-measurement setting. The results of numerical simulation on real-world large-scale network topologies suggest the effectiveness and also the issues of the proposed lossy link location scheme.The 34th International Conference on Information Networking (ICOIN 2020), January 7-10, 2020, Barcelona, Spai

On Retrieval Order of Statistics Information from OpenFlow Switches to Locate Lossy Links by Network Tomographic Refinement

To maintain service quality and availability in managed networks, detecting and locating high loss-rate links (i.e., lossy links that are likely congested or physically unstable) in a fast and light-weight manner is required. In our previous study, we proposed a framework of network-assisted location of lossy links on OpenFlow networks. In the framework, a measurement host launches a series of multicast probe packets traversing all full-duplex links; and then the controller retrieves statistics on the arrival of those probe packets at different input ports on different switches and compares them to locate high loss-rate links. The number of accesses to switches required to locate all lossy links strongly depends on the retrieval order in collecting the statistics and should be small as much as possible. Therefore, in this paper, to minimize the necessary number of accesses, we develop a new location scheme with an appropriate retrieval order using a Bayesian-based network tomography to refine candidates for lossy links. The results of numerical simulation on a real-world topology demonstrate the effectiveness of the new location scheme.11th International Conference on Intelligent Networking and Collaborative Systems(INCoS 2019), September 5-7, 2019, Oita, Japa

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School