Multihoming and Adaptive Multipath Transmission Using Off-the-shelf Components in Preclinical Medical Care

Virtual Network Embedding will be one of the key concepts of the Future Internet. For an ISP it is important to know how many additional Virtual Networks (VNs) of a specific application (e.g. web, streaming, P2P, and VoIP) are mappable into the current resource substrate with a certain probability. In this work we calculate this probability with our embedding algorithm which enables us to consider side effects based on remapping of VNs (e.g. due to reduced link delay). Our results show that minimal extra resources can significantly increase embedding probability of additional VNs.

Todays virtualization technologies are manifold and a comparison is hardly achievable. Thus, a metric independent from the virtualization technology is required to compare different systems. The me ...

Recent developments in network science will facilitate the Internet as we know it today to integrate existing and upcoming technologies into a heterogeneous and highly dynamic resource pool. This enables the design of new applications and services which will form the Future Internet (FI). From this, many interesting prospects and unknown flexibility in terms of resource usage arise. In order to achieve this flexibility and make it usable for Service Providers (SPs) a key concept of the FI will be Virtual Networks (VNs) embedded into this resource pool. Furthermore, Federation which includes a closer interaction, resource sharing, and information exchange between providers will be an enabler for this freedom of design. This allows the operation of heterogeneous and divergent services on the same physical infrastructure substrate. It is clear to see that all these upcoming possibilities offer new areas of research. In this work we structure the parameters required to describe resources and services accurately. Furthermore, we summarize research questions that need to be resolved in order to make the FI a story of success.

Content Distribution Networks serve a large share of today's Internet traffic from a multitude of locations, each in relative proximity to the respective consumers. In this paper, we analyze the performance of the YouTube video platform using Seattle, a distributed Internet testbed running on donated resources (like SETI@home), including equipment on the user's premises. This better reflects the experience seen by true users than dedicated test systems such as PlanetLab. In Seattle, measurements are restricted to the application layer, so we only read from and write into a stream (TCP) network socket, and perform DNS lookups. Using forty vantage points in different geographic regions running for more than 600 hours, we continuously measure the number of IP addresses www.youtube.com resolves to, and approximate the latency to actual video cache servers. We also monitor the latency between the vantage points, and estimate the packet loss. The methodology presented in this paper shows how insights even into large, distributed content delivery systems can be gathered with reasonable effort. The actual results can be of interest to e.g. network operators trying to improve the interworking with CDNs by studying their day-to-day operations for the good of the end user.

Article AquareYoum: Application- and Quality of Experience-Aware Resource Management for YouTube in Wireless Mesh Networks was published on August 1, 2011 in the journal PIK - Praxis der Informationsverarbeitung und Kommunikation (volume 34, issue 3).

The evolution and growing dissemination of the wireless Internet as well as the considerable usage heterogeneity introduced by new fields of applications, raise the need for intelligent resource management in future wireless networks. This necessity is urgent as state-of-the-art wireless resource management is mostly (1) unaware of the Internet applications in the network, (2) enables best effort Internet access only and (3) does not allow application-network interaction. This has been identified as a serious drawback for modern wireless networks and results in different research activities aiming at developing cross-layer and network wide control and management concepts [1]. One approach towards a more intelligent resource management is the idea of application comfort (AC) based resource management [2], [3]. Application comfort quantifies how well an application is currently running and allows a prediction of the quality of experience (QoE). An AC monitoring tool running at the client and cooperating with resource management tools thus allows (1) to guarantee application-specific service levels and (2) to react prior to a QoE degradation. In contrast to conventional solutions, the use of an AC monitoring tool avoids the need of complex flow classification techniques like deep packet inspection, TCP/IP or application header analysis or the use of heuristics, as the tool simply signals the existence of a certain application to the network. Secondly, a QoE-based resource management does not need to know applicationspecific QoS requirements which are often not known or even changing over time as it is e.g. the case for videos with variable bite rate, but is able to adapt the network resources in dependence on the application performance. To illustrate the benefits of AC for a network resource management architecture we propose the following demonstration: A client in a wireless mesh network (WMN) is playing YouTube videos while the YouTube AC monitoring tool YoMo [2] continuously monitors the YouTube AC at the client. We chose the example of WMNs since they are an attractive low-cost option for offering broadband wireless Internet access. Second, practically implementing resource

We present a tool for the analysis of fault-tolerance in packet-switched communication networks. Network elements like links or routers can fail or unexpected traffic surges may occur. They lead to service disruptions and degradations. Our tool quantifies these risks and presents a comprehensive digest of the results. We explain the core idea of the analysis and illustrate the tool.