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This paper explores, primarily by means of analysis, the differences that can exist between individual and aggregate loss guarantees in an environment where guarantees are only provided at an aggregate level. The focus is on understanding which traffic parameters are responsible for inducing possible deviations and to what extent. In addition, we seek to evaluate the level of additional resources, e.g., bandwidth or buffer, required to ensure that all individual loss measures remain below their desired target. The paper's contributions are in developing analytical models that enable the evaluation of individual loss probabilities in settings where only aggregate losses are controlled, and in identifying traffic parameters that play a dominant role in causing differences between individual and aggregate losses. The latter allows the construction of guidelines identifying what kind of traffic can be safely multiplexed into a common service class.
Aggregate, aggregation, quality of service, QoS, loss, Network, 303
Aggregate, aggregation, quality of service, QoS, loss, Network, 303
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 25 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |