We consider a transmission of a delay-sensitive data stream from a single source to a single destination. The reliability of this transmission may suffer from bursty packet losses - the predominant type of failures in today's Internet. An effective and well studied solution to this problem is to protect the data by a Forward Error Correction (FEC) code and send the FEC packets over multiple paths. In this paper we show that the performance of such a multipath FEC scheme can often be further improved. Our key observation is that the propagation times on the available paths often significantly differ, typically by 10-100ms. We propose to exploit these differences by appropriate packet scheduling that we call Spread'. We evaluate our solution with a precise, analytical formulation and trace-driven simulations. Our studies show that Spread substantially outperforms the state-of-the-art solutions. It typically achieves two- to five-fold improvement (reduction) in the effective loss rate. Or conversely, keeping the same level of effective loss rate, Spread significantly decreases the observed delays and helps fighting the delay jitter.
As in @cite_2 @cite_14 @cite_6 @cite_12 we assume the paths to be independent. This can be achieved by detecting correlated paths in end-to-end measurements @cite_16 and treating them as one. Another approach is to find paths that are IP link disjoint, which should be possible if the site is multi-homed. Finally, even if all the available paths are to some extent correlated we can still get some performance benefits @cite_10 @cite_7 @cite_1 @cite_8 , though limited @cite_0 @cite_3 .
@cite_14 @cite_7 @cite_8 @cite_1 @cite_6 @cite_3 @cite_0 @cite_2 @cite_16 @cite_10 @cite_12
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Measuring real-time end-to-end network path performance metrics is important for several distributed applications such as media streaming systems (e.g., for switching to paths with higher bandwidth and lower jitter) and content distribution systems (e.g., for selecting servers with lower latency). However, it is challenging to perform such end-to-end pairwise measurements in large distributed systems while achieving high accuracy and avoid interfering with existing traffic. On the end hosts, the measurements can overload the machine by causing interference among themselves and other processes. On the network, the measurement packets from different hosts can interfere among themselves and with other flows on bottleneck links. In this paper, we propose a system to monitor end-host and network resources and adapt the number of measurements according to the observed load. Our scheme avoids interference by measuring only a small subset of network paths and reconstructing the entire network path properties from the partial, indirect measurements. Our simulation experiments and real testbed experiments on PlanetLab show that our path selection algorithm working with resource constraints does not adversely affect the accuracy of inference and our system can effectively adapt to the changing resource usage at the end hosts. In theory, multi homed Internet hosts, that is, hosts simultaneously connected to multiple Internet service providers (ISP) should see increased access capacity, be able to circumvent possible last-mile congestion problems, and experience improved end-to-end quality of service (QoS). In practice however, the advantages one can gain from multi homing are highly dependent on the path switching mechanism used, that is, on dynamically deciding which ISP should be used as a first-hop. This paper is a first step toward understanding the trade-off between performance improvements multi homing can help achieve and the complexity of the decisions that must be made. We measure changes in end-to-end network layer metrics (loss, latency, jitter) over the different paths available from a multi homed host to a large population of Internet hosts. Our measurements indicate that (1) in over 60 of the cases, one only needs to reevaluate the service provided by each ISP every minute to improve the performance of a specific metric, and that (2) in approximately 85 of the cases, decisions to switch from one ISP to another can be treated independently of the service metric of interest. We conclude that multi homing could in practice result in noticeable performance improvements. In this paper we study the problem of building a constant-degree connected dominating set (CCDS), a network structure that can be used as a communication backbone, in the dual graph radio network model ( in J Parallel Distrib Comput 64:89–96, 2004; in Proceedings of the international symposium on principles of distributed computing 2009, Distrib Comput 24(3–4):187–206 2011, Proceedings of the international symposium on principles of distributed computing 2010). This model includes two types of links: reliable, which always deliver messages, and unreliable, which sometimes fail to deliver messages. Real networks compensate for this differing quality by deploying low-layer detection protocols to filter unreliable from reliable links. With this in mind, we begin by presenting an algorithm that solves the CCDS problem in the dual graph model under the assumption that every process (u ) is provided with a local link detector set consisting of every neighbor connected to (u ) by a reliable link. The algorithm solves the CCDS problem in (O ( ^2 n b + ^3 n ) ) rounds, with high probability, where ( ) is the maximum degree in the reliable link graph, (n ) is the network size, and (b ) is an upper bound in bits on the message size. The algorithm works by first building a Maximal Independent Set (MIS) in ( ^3 n ) time, and then leveraging the local topology knowledge to efficiently connect nearby MIS processes. A natural follow-up question is whether the link detector must be perfectly reliable to solve the CCDS problem. With this in mind, we first describe an algorithm that builds a CCDS in (O( )polylog ((n)) ) time under the assumption of (O(1) ) unreliable links included in each link detector set. We then prove this algorithm to be (almost) tight by showing that the possible inclusion of only a single unreliable link in each process’s local link detector set is sufficient to require ( ( ) ) rounds to solve the CCDS problem, regardless of message size. We conclude by discussing how to apply our algorithm in the setting where the topology of reliable and unreliable links can change over time. In mobile networks, distance variations caused by node mobility generate fluctuations in the channel gains. Such fluctuations can be treated as another type of fading besides multipath effects. In this paper, the interference statistics in mobile random networks are characterized by incorporating the distance variations of mobile nodes to the channel gain fluctuations. The mean interference is calculated at the origin and at the border of a finite mobile network. The network performance is evaluated in terms of the outage probability. Compared to a static network, the interference in a single snapshot does not change under uniform mobility models. However, random waypoint mobility increases (decreases) the interference at the origin (at the border). Furthermore, due to the correlation of the node locations, the interference and outage are temporally and spatially correlated. We quantify the temporal correlation of the interference and outage in mobile Poisson networks in terms of the correlation coefficient and conditional outage probability, respectively. The results show that it is essential that routing, MAC, and retransmission schemes need to be smart (i.e., correlation-aware) to avoid bursts of transmission failures.