Abstract — In this paper, we present the design, implementation, and evaluation of the iPlane, a scalable service providing accurate predictions of Internet path performance for emerging overlay services. Unlike the more common black box latency prediction techniques in use today, the iPlane builds an explanatory model of the Internet. We predict end-to-end performance by composing measured performance of segments of known Internet paths. This method allows us to accurately and efficiently predict latency, bandwidth, capacity and loss rates between arbitrary Internet hosts. We demonstrate the feasibility and utility of the iPlane service by applying it to several representative overlay services in use today: content distribution, swarming peer-to-peer filesharing, and voice-over-IP. In each case, we observe that using iPlane’s predictions leads to a significant improvement in end user performance. 1

building block for automated diagnosis and control

This paper motivates and describes an example network Information Plane, called Sophia, currently deployed on PlanetLab. Sophia is a distributed system that collects, stores, propagates, aggregates, and reacts to observations about the network’s current conditions. Sophia’s approach is novel: it can be viewed as a multi-user distributed expression evaluator in which sensors and actuators form the ground terms, and statements take on the complete expressiveness of a logic language like Prolog. This paper argues that this approach has several advantages in managing and controlling a complex, federated, and evolving network: (1) a declarative logic language provides a natural way to express the kinds of statements that are common to this application domain, through temporal and positional logic rules, facts and expressions; and (2) distributed evaluation of such logic expressions provides many opportunities for performance optimization yielding an efficient system.

ABSTRACT Over the past few years, wireless networking technologies have made vast forays into our daily lives. Today, one can find 802.11 hardware and other personal wireless technology employed at homes, shopping malls, coffee shops and airports. Present-day wireless network deployments bear two important properties: they are unplanned, with most access points (APs) deployed by users in a spontaneous manner, resulting in highly variable AP densities; and they are unmanaged, since manually configuring and managing a wireless network is very complicated. We refer to such wireless deployments as being chaotic.

Changes in the end-to-end path between two hosts can lead to sudden changes in the round-trip time and available bandwidth, or even the complete loss of connectivity. Determining the reason for the routing change is crucial for diagnosing and fixing the problem, and for holding a particular domain accountable for the disruption. Active measurement tools like traceroute can infer the current path between two end-points, but not where and why the path changed. Analyzing BGP data from multiple vantage points seems like a promising way to infer the root cause of routing changes. In this paper, we explain the inherent limitations of using BGP data alone and argue for a distributed approach to troubleshooting routing problems. We propose a solution where each AS continuously maintains a view of routing changes in its own network, without requiring additional support from the underlying routers. Then, we describe how to query the measurement servers along the AS-level forwarding path from the source to the destination to uncover the location and the reason for the routing change.

Networks are hard to manage and in spite of all the so called holistic management packages, things are getting worse. We argue that the difficulty of network management can partly be attributed to a fundamental flaw in the existing architecture: protocols expose all their internal details and hence, the complexity of the ever-evolving data plane encumbers the management plane. Guided by this observation, in this paper we explore an alternative approach and propose Complexity Oblivious Network Management (CONMan), a network architecture in which the management interface of data-plane protocols includes minimal protocol-specific information. This restricts the operational complexity of protocols to their implementation and allows the management plane to achieve high level policies in a structured fashion. We built the CON-Man interface of a few protocols and a management tool that can achieve high-level configuration goals based on this interface. Our preliminary experience with applying this tool to real world VPN configuration indicates the architecture’s potential to alleviate the difficulty of configuration management.

Deployments of wireless LANs consisting of hundreds of 802.11 access points with a large number of users have been reported in enterprises as well as college campuses. However, due to the unreliable nature of wireless links, users frequently encounter degraded performance and lack of coverage. This problem is even worse in unplanned networks, such as the numerous access points deployed by homeowners. Existing approaches that aim to diagnose these problems are inefficient because they troubleshoot at too high a level, and are unable to distinguish among the root causes of degradation. This paper designs, implements, and tests fine-grained detection algorithms that are capable of distinguishing between root causes of wireless anomalies at the depth of the physical layer. An important property that emerges from our system is that diagnostic observations are combined from multiple sources over multiple time instances for improved accuracy and efficiency.

This paper presents a highly efficient and accurate link-quality measurement framework, called EAR (Efficient and Accurate link-quality monitoR), for multi-hop wireless mesh networks, that has several salient features. First, it exploits three complementary measurement schemes: passive, cooperative, and active monitoring. EAR maximizes the measurement accuracy by (i) dynamically and adaptively adopting one of these schemes and (ii) opportunistically exploiting the unicast application traffic present in the network, while minimizing the measurement overhead. Second, EAR effectively identifies the existence of wireless link asymmetry by measuring the quality of each link in both directions of the link, thus improving the utilization of network capacity by up to 114%. Finally, its reliance on both the network layer and the IEEE 802.11-based device driver solutions makes EAR easily deployable in existing multi-hop wireless mesh networks without system recompilation or MAC firmware modification. EAR has been evaluated extensively via both ns-2-based simulation and experimentation on our Linux-based implementation. Both simulation and experimentation results have shown EAR to provide highly accurate link-quality measurements with minimum overhead.

The Internet is transparent to success but opaque to failure. This veil of ignorance prevents ISPs from detecting failures by peering partners, and hosts from intelligently adapting their routes to adverse network conditions. To rectify this, we propose an accountability framework that would tell hosts where their packets have died. We describe a preliminary version of this framework and discuss its viability.

Our work is motivated by two observations about the state of networks today. Operators have little visibility into the end users' network experience while end users have little information or recourse when they encounter problems. We propose a system called NetProfiler, in which end hosts share network performance information with other hosts over a peer-to-peer network. The aggregated information from multiple hosts allows NetProfiler to profile the wide-area network, i.e., monitor end-to-end performance, and detect and diagnose problems from the perspective of end hosts. We define a set of attribute hierarchies associated with end hosts and their network connectivity. Information on the network performance and failures experienced by end hosts is then aggregated along these hierarchies, to identify patterns (e.g., shared attributes) that might be indicative of the source of the problem. In some cases, such sharing of information can also enable end hosts to resolve problems by themselves. The results from a 4-week-long Internet experiment indicate the promise of this approach.