Pulp: An adaptive gossip-based dissemination protocol for multi-source message streams

Gossip-based protocols provide a simple, scalable, and robust way to disseminate messages in large-scale systems. In such protocols, messages are spread in an epidemic manner. Gossiping may take place between nodes using push, pull, or a combination. Push-based systems achieve reasonable latency and high resilience to failures but may impose an unnecessarily large redundancy and overhead on the system. At the other extreme, pull-based protocols impose a lower overhead on the network at the price of increased latencies. A few hybrid approaches have been proposed—typically pushing control messages and pulling data—to avoid the redundancy of high-volume content and single-source streams. Yet, to the best of our knowledge, no other system intermingles push and pull in a multiple-senders scenario, in such a way that data messages of one help in carrying control messages of the other and in adaptively adjusting its rate of operation, further reducing overall cost and improving both on delays and robustness. In this paper, we propose an efficient generic push-pull dissemination protocol, Pulp, which combines the best of both worlds. Pulp exploits the efficiency of push approaches, while limiting redundant messages and therefore imposing a low overhead, as pull protocols do. Pulp leverages the dissemination of multiple messages from diverse sources: by exploiting the push phase of messages to transmit information about other disseminations, Pulp enables an efficient pulling of other messages, which themselves help in turn with the dissemination of pending messages. We deployed Pulp on a cluster and on PlanetLab. Our results demonstrate that Pulp achieves an appealing trade-off between coverage, message redundancy, and propagation delay.

[1]  Anne-Marie Kermarrec,et al.  Peer-to-Peer Membership Management for Gossip-Based Protocols , 2003, IEEE Trans. Computers.

[2]  R. Guerraoui,et al.  Gossiping Capabilities , 2008 .

[3]  Laurent Massoulié,et al.  Is There a Future for Mesh-Based live Video Streaming? , 2008, 2008 Eighth International Conference on Peer-to-Peer Computing.

[4]  Scott Shenker,et al.  Epidemic algorithms for replicated database maintenance , 1988, OPSR.

[5]  Pascal Felber,et al.  SPLAY: Distributed Systems Evaluation Made Simple (or How to Turn Ideas into Live Systems in a Breeze) , 2009, NSDI.

[6]  Vinay S. Pai,et al.  Chainsaw: Eliminating Trees from Overlay Multicast , 2005, IPTPS.

[7]  Stefan Savage,et al.  Understanding Availability , 2003, IPTPS.

[8]  Kenneth P. Birman,et al.  Bimodal multicast , 1999, TOCS.

[9]  Anne-Marie Kermarrec,et al.  Lightweight probabilistic broadcast , 2003, TOCS.

[10]  Bo Li,et al.  Inside the New Coolstreaming: Principles, Measurements and Performance Implications , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[11]  Laurent Massoulié,et al.  Epidemic live streaming: optimal performance trade-offs , 2008, SIGMETRICS '08.

[12]  Keith Marzullo,et al.  Gossip versus Deterministic Flooding: Low Message Overhead and High Reliability for Broadcasting on Small Networks , 1999 .

[13]  Michael Dahlin,et al.  BAR gossip , 2006, OSDI '06.

[14]  Alessandro Russo,et al.  Delay-Aware Push/Pull Protocols for Live Video Streaming in P2P Systems , 2010, 2010 IEEE International Conference on Communications.

[15]  Stefan Schmid,et al.  Push-to-Pull Peer-to-Peer Live Streaming , 2007, DISC.

[16]  Richard M. Karp,et al.  Randomized rumor spreading , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[17]  Bo Li,et al.  DONet: A Data-Driven Overlay Network For Efficient Live Media Streaming , 2004, INFOCOM 2005.

[18]  Indranil Gupta,et al.  Active and passive techniques for group size estimation in large-scale and dynamic distributed systems , 2007, J. Syst. Softw..

[19]  Anne-Marie Kermarrec,et al.  Gossiping in distributed systems , 2007, OPSR.

[20]  Keith Marzullo,et al.  Directional Gossip: Gossip in a Wide Area Network , 1999, EDCC.

[21]  Anne-Marie Kermarrec,et al.  Gossip-based peer sampling , 2007, TOCS.

[22]  Anne-Marie Kermarrec,et al.  NAT-resilient Gossip Peer Sampling , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[23]  Prashant J. Shenoy,et al.  Consistency maintenance in dynamic peer-to-peer overlay networks , 2006, Comput. Networks.

[24]  Robbert van Renesse,et al.  A Gossip-Style Failure Detection Service , 2009 .

[25]  Rajeev Rastogi,et al.  Efficient gossip-based aggregate computation , 2006, PODS.

[26]  Prashant J. Shenoy,et al.  Maintaining mutual consistency for cached Web objects , 2001, Proceedings 21st International Conference on Distributed Computing Systems.

[27]  R. Lo Cigno,et al.  On some fundamental properties of P2P push/pull protocols , 2008, 2008 Second International Conference on Communications and Electronics.

[28]  B. Cohen,et al.  Incentives Build Robustness in Bit-Torrent , 2003 .

[29]  Michael Dahlin,et al.  FlightPath: Obedience vs. Choice in Cooperative Services , 2008, OSDI.

[30]  Bo Li,et al.  CoolStreaming/DONet: a data-driven overlay network for peer-to-peer live media streaming , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[31]  Anne-Marie Kermarrec,et al.  Heterogeneous Gossip , 2009, Middleware.

[32]  Anne-Marie Kermarrec,et al.  Probabilistic Reliable Dissemination in Large-Scale Systems , 2003, IEEE Trans. Parallel Distributed Syst..

[33]  Laurent Massoulié,et al.  Gossiping With Multiple Messages , 2006, IEEE Transactions on Information Theory.

[34]  Anne-Marie Kermarrec,et al.  FoG: Fighting the Achilles' Heel of Gossip Protocols with Fountain Codes , 2009, SSS.

[35]  Krithi Ramamritham,et al.  Maintaining temporal coherency of virtual data warehouses , 1998, Proceedings 19th IEEE Real-Time Systems Symposium (Cat. No.98CB36279).

[36]  Maarten van Steen,et al.  CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays , 2005, Journal of Network and Systems Management.