Broadcast with time and causality constraints for multimedia applications

Baldoni, Roberto; Raynal, Michel; Prakash, Ravi; Singhal, Mukesh

doi:10.1109/eurmic.1996.546489

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Our protocol can also be seen as a "soft" real-time protocol, with connections to such work as the ⌬-T protocol developed by Cristian et al [1985], and Baldoni et al's ␦-causal protocol [Baldoni et al 1996a;1996b]. None of this prior work investigated the issue of steady load and steady data delivery during failures, nor does the prior work scale particularly well.…”

Section: Comparison With Prior Workmentioning

confidence: 92%

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Bimodal multicast

Birman

Hayden

Özkasap

et al. 1999

ACM Trans. Comput. Syst.

498

523

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There are many methods for making a multicast protocol "reliable." At one end of the spectrum, a reliable multicast protocol might offer atomicity guarantees, such as all-ornothing delivery, delivery ordering, and perhaps additional properties such as virtually synchronous addressing. At the other are protocols that use local repair to overcome transient packet loss in the network, offering "best effort" reliability. Yet none of this prior work has treated stability of multicast delivery as a basic reliability property, such as might be needed in an internet radio, television, or conferencing application. This article looks at reliability with a new goal: development of a multicast protocol which is reliable in a sense that can be rigorously quantified and includes throughput stability guarantees. We characterize this new protocol as a "bimodal multicast" in reference to its reliability model, which corresponds to a family of bimodal probability distributions. Here, we introduce the protocol, provide a theoretical analysis of its behavior, review experimental results, and discuss some candidate applications. These confirm that bimodal multicast is reliable, scalable, and that the protocol provides remarkably stable delivery throughput. PREFACEEncamped on the hilltops overlooking the enemy fortress, the commanding General prepared for the final battle of the campaign. Given the information he was gathering about enemy positions, his forces could prevail. Indeed, if most of his observations could be communicated to most of his forces the battle could be won even if some reports reached none or very few of his troops. But if many reports failed to get through, or reached many but not most of his commanders, their attack would be uncoordinated and the battle lost, for only he was within direct sight of the enemy, and in the coming battle strategy would depend critically upon the quality of the information at hand.Although the General had anticipated such a possibility, his situation was delicate. As the night wore on, he dispatched wave upon wave of updates on the enemy troop placements. Some couriers perished in the dark, wet forests separating the camps. Worse still, some of his camps were beset by the disease that had ravaged the allies since the start of the campaign. They could not be relied upon, as chaos and death ruled there.With the approach of dawn, the General sat sipping coffee-rotgut stuffreflectively. In the night, couriers came and went, following secret protocols worked out during the summer. At the appointed hour, he rose to lead the attack. The General was not one to shirk a calculated risk.

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Section: Comparison With Prior Workmentioning

confidence: 92%

“…Our protocol can be understood as offering a form of weak real-time guarantee; relevant prior work includes Cristian et al [1985; and Baldoni et al [1996a;1996b].…”

Section: A Bimodal Multicast Protocolmentioning

confidence: 99%

Bimodal multicast

Birman

Hayden

Özkasap

et al. 1999

ACM Trans. Comput. Syst.

498

523

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“…It is worth emphasizing that causal message ordering is of prime interest to the design of many distributed applications. Examples of them are event notification systems [25], multimedia applications [7,30], multi-part online games [16], systems that provide distributed replicated causal data consistency [4], distributed snapshots [1], distributed database [38], shared objects [29], publish/subscribe systems [13,24], etc.…”

Section: One Tree Per Sourcementioning

confidence: 99%

A Communication-Efficient Causal Broadcast Protocol

Araujo

Arantes

Júnior

et al. 2018

Proceedings of the 47th International Conference on Parallel Processing

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A causal broadcast ensures that messages are delivered to all nodes (processes) preserving causal relation of the messages. In this paper, we propose a causal broadcast protocol for distributed systems whose nodes are logically organized in a virtual hypercube-like topology called VCube. Messages are broadcast by dynamically building spanning trees rooted in the message's source node. By using multiple trees, the contention bottleneck problem of a single root spanning tree approach is avoided. Furthermore, different trees can intersect at some node. Hence, by taking advantage of both the out-of-order reception of causally related messages at a node and these paths intersections, a node can delay to one or more of its children in the tree, the forwarding of the messages whose some causal dependencies it knows that the children in question can not satisfy yet. Such a delay does not induce any overhead. Experimental evaluation conducted on top of PeerSim simulator confirms the communication effectiveness of our causal broadcast protocol in terms of latency and message traffic reduction.

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“…Although an interconnection server introduces a short forwarding step, some papers (e.g., [56]) criticise that such application-level processing introduces a delay that might not be affordable by all kinds of applications. For instance, media stream multicasting should comply with soft real-time constraints and demand specific causal multicast protocols that tolerate message losses [72,11,15].…”

Section: Interconnection Approachmentioning

confidence: 99%

Scalability approaches for causal multicast: a survey

et al. 2015

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Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability those applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast may propagate updates in a pre-defined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different scalability approaches for reliable causal multicast protocols.

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Broadcast with time and causality constraints for multimedia applications

Cited by 6 publications

References 16 publications

Bimodal multicast

Bimodal multicast

A Communication-Efficient Causal Broadcast Protocol

Scalability approaches for causal multicast: a survey

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