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Incorporating contribution-awareness into mesh-based Peer-to-Peer streaming systems

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Abstract

While Peer-to-Peer streaming has become increasingly popular over the Internet during recent years, the proper allocation of available resources among peers in a resource constraint environment, remains a challenging problem. In a resource constraint environment, the allocated resources and thus delivered quality to individual peers should be proportional to their contribution to the system, i.e., resource allocation should be contribution aware. This in turn results in fairness among peers and encourages active contribution from participating peers which is essential for scalability of P2P systems. However, contribution-aware resource allocation is challenging due to the distributed and dynamic nature of resources in P2P systems. In this paper, we present a tax-based contribution-aware scheme for live mesh-based P2P streaming approaches. In our proposed scheme, individual peers use a tax function to determine their number of parent peers (i.e., their share of resources) based on the number of their child peers (i.e., peers’ contributed resources) and the aggregate available resources in the system. We examine the behavior of a commonly used tax function, and describe how the contribution aware scheme can leverage the tax function. Through extensive simulations we demonstrate the ability of our proposed scheme to properly allocate available resources among participating peers over a wide range of scenarios. We show that the amount of resources (i.e., bandwidth) is divided across peers proportional to their contribution and in our default simulation setting the median delivered quality to high bandwidth peers with high contribution is improved by 100%. We believe that our results shed an insightful light on the dynamics of resource utilization and allocation in the context of live mesh-based P2P streaming.

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Notes

  1. An undirected overlay is a special case of directed overlay, and thus most of our discussion and findings are still valid.

  2. Note that, many previous studies have used this intuition to use MDC coding in their approach [12, 13, 18].

  3. Note that, the choice of bwpf is limited by the peer with minimum bandwidth contribution and it can be set to a fraction of it.

  4. Assuming cooperative users is not unrealistic since one can use incentive schemes [23–25] to ensure contribution of resources or deploy a P2P streaming system in a closed setting (e.g., within setup boxes) to achieve the same goal.

  5. In contrast, the contribution aware scheme for tree-based P2P streaming [12] must specifically label each connection because each connection provides a particular description.

  6. All other states that a parent might need can be derived from these information.

  7. While this figure shows the tax function for positive tax rates values, in practice only tax values that are larger than 1, are of interest.

  8. Note that, real world experiments and packet-level simulations are often useful to evaluate the protocol in a realistic setting such as realistic packet level dynamics (and background traffic), and bandwidth and RTT heterogeneity. However, we focus on session-level simulations as follows: the contribution-aware mechanism assumes all connections have the same value and primarily controls resource allocation by adjusting the incoming degree of the overlay. Therefore, this mechanism is not affected by packet level dynamics, bandwidth or RTT variations.

  9. One can compare the performance of tax-based contribution-awareness in both tree- and mesh-based approaches. However, due to the inherent differences between these two approaches [11], any observed differences in the performance of contribution-aware mechanism in tree and mesh-based will be related to major differences between them.

  10. It is worth noting that En-En and Ex-Ex policies might affect the allocation of resources when RI significantly changes with time. However, constructing such a scenario requires detail information about potential dynamics of RI over time that has not been provided by previous empirical studies. We plan to further study this issue in our future work.

  11. Note that normalizing the rate of change in parents due to preemption in Fig. 4c is not meaningful since the observed rate depends on the relative number of excess connections for each peer.

  12. Note that the total population changes with churn but psim can set the arrival rate in order to keep the average population at a desired number.

  13. One can generate artificial group dynamics that leads to significant and rapid changes in RI. However, such dynamics appear to be unrealistic since it is inconsistent with the reported peer arrival and peer session times in previous empirical studies.

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Acknowledgements

This material is based upon work supported in part by the NSF CAREER Award CNS-0448639. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Oregon, Eugene, OR, 97403-1202, USA

    Nazanin Magharei & Reza Rejaie

  2. Research and Innovation, Technicolor, 2 Independence Way, Princeton, NJ, 08540, USA

    Yang Guo

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  1. Nazanin Magharei
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Correspondence to Nazanin Magharei.

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Magharei, N., Rejaie, R. & Guo, Y. Incorporating contribution-awareness into mesh-based Peer-to-Peer streaming systems. Peer-to-Peer Netw. Appl. 4, 231–250 (2011). https://doi.org/10.1007/s12083-010-0078-y

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