Summary Development of workflow and data management systems are challenging because of the need to provide a service for users with varying degrees of expertise from novices to experts with more knowledge than the developers. We have received feedback from users and developers on the functionality and usability of the Swiss Grid Proteomics Portal, now called iPortal, during its first year of operation. Monitoring and interaction with the production system under heavy use have provided further information on how to improve efficiency and stability. In a second, highly upgraded version of iPortal, we have introduced several new concepts based on this feedback. In this paper, we detail the requirements and the improvements we have made, and also give an outlook on future directions. Copyright © 2014 John Wiley & Sons, Ltd.

To manage and monitor their networks in a proper way, network operators are often interested in automatic methods that enable them to identify applications generating the traffic traveling through their networks as fast (i.e., from the first few packets) as possible. State-of-the-art packet-based traffic classification methods are either based on costly inspection of the payload of several packets in each flow or on basic flow statistics without taking into account the packet content. In this paper, we consider an intermediate approach of analyzing only the first few bytes of the first (or first few) packet(s) of each flow and propose automatic, machine-learning-based methods with very low computational complexity and memory footprint. The performance of these techniques are thoroughly analyzed, showing that outstanding early classification accuracy can be achieved on traffic traces generated by a diverse set of applications (including P2P TV and file sharing) in a laboratory environment as well as on a real-world data set collected in the network of a large European ISP.

To manage and monitor their networks in a proper way, network operators are often interested in identifying the applications generating the traffic traveling through their networks, and doing it as fast (i.e., from as few packets) as possible. State-of-the-art packet-based traffic classification methods are either based on the costly inspection of the payload of several packets of each flow or on basic flow statistics that do not take into account the packet content. In this paper we consider the intermediate approach of analyzing only the first few bytes of the first (or first few) packets of each flow. We propose automatic, machine-learning-based methods achieving remarkably good early classification performance on real traffic traces generated from a diverse set of applications (including several versions of P2P TV and file sharing), while requiring only limited computational and memory resources.