Enforceable and Eecient Service Provisioning ?

Provisioning resources for network services introduces the connicting requirement for both deter-ministic traac models to isolate and police users, and statistical multiplexing to eeciently utilize and share network resources. We address this issue by introducing two complimentary schemes for QoS management for deterministically policed ows. The rst is adversarial mode resource allocation: here we bound the stochastic envelopes of policed ows and achieve a statistically multiplexed QoS-controlled service, even in the case that all ows are independently adversarial, i.e., when all ows are non-collusively behaving in a worst-case manner at all time scales within the constraints of their policing functions. The second scheme is non-adversarial mode, maximum-entropy allocation: here we determine the maximum-entropy stochastic envelopes of policed (but non-worst-case) ows. Consequently, this scheme exploits a further statistical multiplexing gain via a characterization of the \most likely" behavior of policed ows. Our key technique is to study the problem within the domain of deterministic and stochastic traac envelopes, which allows us to explicitly consider sources with rate variations over multiple time scales, obtain results for any deterministic traac model, and design accurate admission control tests for buuered priority schedulers. We evaluate the schemes' performance with experiments using traces of compressed video and single and dual timescale periodic sources and show that substantial statistical multiplexing gains are achieved.