IP addresses are commonly used to identify hosts or properties of hosts. The address assigned to a host may change, however, and the extent to which these changes occur in time as well as in the address space is currently unknown, especially in IPv6.

Thorup-Zwick (TZ) compact routing guarantees sublinear state growth with the size of the network by routing via landmarks and incurring some path stretch. It uses a pseudo-random landmark selection designed for static graphs, and unsuitable for Internet routing. We propose a landmark selection algorithm for the Internet AS graph that uses k-shells decomposition to choose landmarks. Using snapshots of the AS graph from 1997-2010, we demonstrate that the ASes in the kmax-shell are highly-stable over time, and form a sufficient landmark set for TZ routing in the overwhelming majority of cases (in the remainder, adding the next k-shell suffices). We evaluate path stretch and forwarding table sizes, and show that these landmark sets retain low average path stretch with tiny forwarding tables, but are better suited to the dynamic nature of the AS graph than the original TZ landmark selection algorithm.

Spotify is a popular music-streaming service which has seen widespread use across Europe. While Spotify's server- side behaviour has previously been studied, little is known about the client-side behaviour. In this paper, we describe an experimental study where we collect packet headers for Spotify traffic over multiple 24-hour time frames at a client host. Two distinct types of behaviour are observed, when tracks are being downloaded, and when the client is only serving requests from other peers. We also note wide variation in connection lifetimes, as seen in other studies of peer-to-peer systems. These findings are relevant for improving Spotify itself, and for the designers of other hybrid peer-to-peer and server-based distribution architectures.

Many residential and small business users connect to the Internet via home gateways, such as DSL and cable modems. The characteristics of these devices heavily influence the quality and performance of the Internet service that these users receive. Anecdotal evidence suggests that an extremely diverse set of behaviors exists in the deployed base, forcing application developers to design for the lowest common denominator. This paper experimentally analyzes some characteristics of a substantial number of different home gateways: binding timeouts, queuing delays, throughput, protocol support and others.

Abstract Future e‐Health systems will consist of low‐power on‐body wireless sensors attached to mobile users that interact with an ubiquitous computing environment to monitor the health and well being of patients in hospitals or at home. Patients or health practitioners have very little technical computing expertise so these systems need to be self‐configuring and self‐managing with little or no user input. More importantly, they should adapt autonomously to changes resulting from user activity, device failure, and the addition or loss of services. We propose the Self‐Managed Cell (SMC) as an architectural pattern for all such types of ubiquitous computing applications and use an e‐Health application in which on‐body sensors are used to monitor a patient living in their home as an exemplar. We describe the services comprising the SMC and discuss cross‐SMC interactions as well as the composition of SMCs into larger structures. Copyright © 2007 John Wiley & Sons, Ltd.

Body sensor networks consisting of low-power on- body wireless sensors attached to mobile users will be used in the future to monitor the health and well being of patients in hospitals or at home. Such systems need to adapt autonomously to changes in context, user activity, device failure, and the availability or loss of services. To this end, we propose a policy- based architecture that uses the concept of a Self-Managed Cell (SMC) to integrate services, managed resources and a policy interpreter by means of an event bus. Policies permit the declarative specification of adaptation strategy for self- configuration and self-management. We present the design and implementation of the SMC and describe its potential use in a scenario for management of heart monitoring. Preliminary performance measurements are also presented and discussed.