Group communication techniques in overlay networks

Vik, Knut-Helge; Halvorsen, Paal; Griwodz, Carsten; Zimmermann, Roger; Roedig, Utz

doi:10.1145/1662529.1662532

Cited by 4 publications

(5 citation statements)

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“…The RTT measurements were obtained in two different manners using tcpinfo or ping (but the results are very similar to [43] and the plots below therefore only how the tcpinfo results). The packet rate was varied because a higher rate follows the actual latency development more closely, while it is also consuming more bandwidth itself, at least in the active measurements.…”

Section: Latency Estimationmentioning

confidence: 65%

“…These simple algorithms form powerful techniques in the search for suitable hosts to migrate game state to. We have tested several algorithms [43] and present the two most prominent, in the following. Both k-Median and k-Center find optimal solutions to two different graph theoretical problems.…”

Section: Core Selection Algorithmsmentioning

confidence: 99%

“…To test our proxy (core) selection algorithms, we have simulated several algorithms [43] mimicking group communication in a game, and we present the results from two of the most promising: k-median and k-center (Sections 4.1.1 and 4.1.2, respectively).…”

Section: Core Selectionmentioning

confidence: 99%

“…As a measure of the estimation accuracy, we use the metric directed relative error, i.e., ping-measured all-to-all RTTs compared to the latency estimates for each pair of PlanetLab nodes (for other metrics, see [43]). A scatterplot of the results is shown in Fig.…”

Section: Estimate Accuracymentioning

confidence: 99%

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The partial migration of game state and dynamic server selection to reduce latency

Beskow

Vik

Halvorsen

et al. 2009

Multimed Tools Appl

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Massively multi-player online games (MMOGs) have stringent latency requirements and must support large numbers of concurrent players. To handle these conflicting requirements, it is common to divide the virtual environment into virtual regions. As MMOGs are world-spanning games, it is plausible to disperse these regions on geographically distributed servers. Core selection can then be applied to locate an optimal server for placing a region, based on player latencies. Functionality for migrating objects supports this objective, with a distributed name server ensuring that references to the moved objects are maintained. As a result we anticipate a decrease in the aggregate latency for the affected players. The core selection relies on a set of servers and measurements of the interacting players latencies. Measuring these latencies by actively probing the network is not scalable for a large number of players. We therefore explore the use of latency estimation techniques to gather this information.

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Section: Latency Estimationmentioning

confidence: 65%

Section: Core Selection Algorithmsmentioning

confidence: 99%

Section: Core Selectionmentioning

confidence: 99%

Section: Estimate Accuracymentioning

confidence: 99%

See 2 more Smart Citations

The partial migration of game state and dynamic server selection to reduce latency

Beskow

Vik

Halvorsen

et al. 2009

Multimed Tools Appl

Self Cite

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“…One major issue with the P2P model is that it can present users with a large latency variance if the selection of peers and tree topology is not carefully chosen. Wisely choosing central "super-peers" and optimizing the construction of the overlay can significantly influence the path latency [42]. The tradeoff for optimizing the topology is that time must be spent calculating optimal topologies, increasing the time it takes before a topology for lower latency is established.…”

Section: ) Structured Peer-to-peermentioning

confidence: 99%

Reducing Internet Latency: A Survey of Techniques and Their Merits

Briscoe

Brunström

Petlund

et al. 2016

IEEE Commun. Surv. Tutorials

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149

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Latency is increasingly becoming a performance bottleneck for Internet Protocol (IP) networks, but historically networks have been designed with aims of maximizing throughput and utilization. This article offers a broad survey of techniques aimed at tackling latency in the literature up to August 2014, and their merits. A goal of this work is to be able to quantify and compare the merits of the different Internet latency reducing techniques, contrasting their gains in delay reduction versus the pain required to implement and deploy them. We found that classifying techniques according to the sources of delay they alleviate provided the best insight into the following issues: 1) the structural arrangement of a network, such as placement of servers and suboptimal routes, can contribute significantly to latency; 2) each interaction between communicating endpoints adds a Round Trip Time (RTT) to latency, especially significant for short flows; 3) in addition to base propagation delay, several sources of delay accumulate along transmission paths, today intermittently dominated by queuing delays; 4) it takes time to sense and use available capacity, with overuse inflicting latency on other flows sharing the capacity; and 5) within end systems delay sources include operating system buffering, head-of-line blocking, and hardware interaction. No single source of delay dominates in all cases, and many of these sources are spasmodic and highly variable. Solutions addressing these sources often both reduce the overall latency and make it more predictable.

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