This paper studies the quality of service (QoS) provision problem in noncooperative networks where applications or users are selfish and routers implement generalized processor sharing (GPS)-based packet scheduling. First, we formulate a model of QoS provision in noncõ perative networks where users are given the freedom to choose both the service classes and traffic volume allocated, and heterogeneousQoS preferences are captured by individual utility functions. We present a comprehensive analysis of the noncooperative multi-class QoS provision game, giving a complete characterization of Nash equilibria and their existence criteria, and show under what conditions they are Pareto and system optimal We show that, in general, Nash equilibria need not exist, and when they do exist, they need not be Pareto nor system optimal-However, we show that for certain "resource-plentiful" systems, the world indeed can be nice with Nash equilibria, Pareto optima, and system optima collapsing into a single class. Second, we study the problem of fac-ditating efiective QoS in systems with multi-dimensional QoS vectors containing q Pennission 10makedigital orhardcopiesof all orpartof thisworkfor personal orclassroom useisgranted without fm pro~ided thatcopies arenotmadeordistriiuled forprotitorcomrnemial adwomgeandthat copiesbearthisnoticeandthefullcitation onthefirstpage.To copy otherwise, to republish, to postonsen,ersortoredism-bute to lists, requires priorspecificpermission and~or a fea ICE 9S CharlestonSC LISA Copyright ACM 19981-581 13-076-7/98/10...S5.00Chen$ both mean-and burstiness-related QoS measures. We extend the game-theoretic analysis to multi-dimensional QoS vector games and show under what conditions the aforementioned results carry over. Motivated by the same context, we study the impact of burstiness under multiple QoS measures on the properties of the induced QoS levels rendered by the service classes in the system. We show that under bursty traffic conditions, it is, in general, impossible for a service class to deliver quality of service superior in both mean-and burstiness-related QoS measures.1We will use the terms wers, applicafions, and sometimes, pkyers, interehangeably.ing of networkresourceswhich are then packagedand made available as high-level services to the user. Aspects of such activities may be modeled as coalition behavior.

Resolution is an important index for evaluating the reconstruction performance of temperature distributions in a combustion environment, and a higher resolution is necessary to obtain more precise combustion diagnoses. Tunable diode laser absorption tomography (TDLAT) has proven to be a powerful combustion diagnosis method for efficient detection. However, restricted by the line-of-sight (LOS) measurement, the reconstruction resolution of TDLAT was dependent on the size of the detection data, which made it difficult to obtain sufficient data for extreme environmental measurements. This severely limits the development of TDLAT in combustion diagnosis. To overcome this limitation, we proposed a super-resolution reconstruction method based on the super-resolution residual U-Net (SRResUNet) to improve the reconstruction resolution using a software method that could take full advantage of residual networks and U-Net to extract the deep features from the limited data of TDLAT to reconstruct the temperature distribution efficiently. A simulation study was conducted to investigate how the parameters would affect the performance of the super-resolution model and to optimize the reconstruction. The results show that our SRResUNet model can effectively improve the accuracy of reconstruction with super-resolution, with good antinoise performance, with the errors of 2-, 4-, and 8-times super-resolution reconstructions of approximately 5.3, 7.4, and 9.7%, respectively. The successful demonstration of SRResUNet in this work indicates the possible applications of other deep learning methods, such as enhanced super-resolution generative adversarial networks (ESRGANs) for limited-data TDLAT.

With the proliferation of high-speed networks and networked services, provisioning differentiated services to a diverse user base with heterogeneous QoS requirements has become an important problem. The traditional approach of resource reservation and admission control provides both guarantees and graded services, however, at the cost of potentially underutilized resources and limited scalability. In this paper, we describe a WAN QoS provision architecture that adaptively organizes best-effort bandwidth into stratified services with graded QoS properties such that the QoS needs of a diverse user base can be effectively met.Our architecture-SBS (Stratified Best-effort Service)-promotes a simple user/simple network realization where neither the user nor the network is burdened with complex computational responsibilities. SBS is scalable, efficient, and adaptive, and it complements the guaranteed service architecture, sharing a common network substrate comprised of GPS routers. It is also a functional complement, provisioning QoS efficiently commensurate with user needs, albeit at the cost of weaker protection. SBS is suited to noncooperative network environments where users behave selfishly and resource contention resolution is mediated by the principle of competitive interaction. A principal feature of SBS is the transformation of user-centric QoS provision mechanisms-a defining characteristic of competitive interaction entailing intimate user control of internal network resources-into network-centric mechanisms while preserving the former's resource allocation paradigm.End-to-end QoS control is facilitated by decentralized control based on Lagrangian optimization-achieve a target end-to-end QoS at minimum cost or resource usage-which, in turn, is amenable to distributed implementation. SBS achieves per-flow QoS control with zero per-flow state at routers and a packet header whose size is independent of hop count. SBS, in spite of foregoing both resource reservation and admission control, is able to provision stable, graded QoS.

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