A basic calculus is presented for stochastic service guarantee analysis in communication networks. Central to the calculus are two definitions, maximum-(virtual)-backlog-centric (m.b.c) stochastic arrival curve and stochastic service curve, which respectively generalize arrival curve and service curve in the deterministic network calculus framework. With m.b.c stochastic arrival curve and stochastic service curve, various basic results are derived under the (min, +) algebra for the general case analysis, which are crucial to the development of stochastic network calculus. These results include (i) superposition of flows, (ii) concatenation of servers, (iii) output characterization, (iv) per-flow service under aggregation, and (v) stochastic backlog and delay guarantees. In addition, to perform independent case analysis, stochastic strict server is defined, which uses an ideal service process and an impairment process to characterize a server. The concept of stochastic strict server not only allows us to improve the basic results (i) -- (v) under the independent case, but also provides a convenient way to find the stochastic service curve of a serve. Moreover, an approach is introduced to find the m.b.c stochastic arrival curve of a flow and the stochastic service curve of a server.
Table summarizes the properties that are provided by the combination of a traffic model, chosen from t.a.c, v.b.c and m.b.c stochastic arrival curves, and a server model, chosen from weak stochastic service curve and stochastic service curve, without any additional constraints on the traffic model or the server model. In Section , we have discussed that under the context of network calculus, most traffic models used in the literature @cite_32 @cite_7 @cite_4 @cite_14 @cite_1 @cite_18 @cite_5 @cite_12 @cite_28 @cite_21 @cite_11 belong to t.a.c and v.b.c stochastic arrival curve, and most server models @cite_17 @cite_7 @cite_1 @cite_18 @cite_5 @cite_12 @cite_11 belong to weak stochastic service curve. Table shows that without additional constraints, these works can only support part of the five required properties for the stochastic network calculus. In contrast, with m.b.c stochastic arrival curve and stochastic service curve, all these properties have been proved in this section.
@cite_18 @cite_14 @cite_4 @cite_7 @cite_28 @cite_21 @cite_1 @cite_32 @cite_17 @cite_5 @cite_12 @cite_11
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A basic calculus is presented for stochastic service guarantee analysis in communication networks. Central to the calculus are two definitions, maximum-(virtual)-backlog-centric (m. b. c) stochastic arrival curve and stochastic service curve, which respectively generalize arrival curve and service curve in the deterministic network calculus framework. With m. b. c stochastic arrival curve and stochastic service curve, various basic results are derived under the (min, +)algebra for the general case analysis, which are crucial to the development of stochastic network calculus. These results include (i)superposition of flows, (ii)concatenation of servers, (iii) output characterization, (iv)per-flow service under aggregation, and (v)stochastic backlog and delay guarantees. In addition, to perform independent case analysis, stochastic strict server is defined, which uses an ideal service process and an impairment process to characterize a server. The concept of stochastic strict server not only allows us to improve the basic results (i)-(v)under the independent case, but also provides a convenient way to find the stochastic service curve of a serve. Moreover, an approach is introduced to find the m.b.c stochastic arrival curve of a flow and the stochastic service curve of a server. In 1991, D. J. Bertsimas and G. van Ryzin introduced and analyzed a model for stochastic and dynamic vehicle routing in which a single, uncapacitated vehicle traveling at a constant velocity in a Euclidean region must service demands whose time of arrival, location and on-site service are stochastic. The objective is to find a policy to service demands over an infinite horizon that minimizes the expected system time (wait plus service) of the demands. This paper extends our analysis in several directions. First, we analyze the problem of m identical vehicles with unlimited capacity and show that in heavy traffic the system time is reduced by a factor of 1 m2 over the single-server case. One of these policies improves by a factor of two on the best known system time for the single-server case. We then consider the case in which each vehicle can serve at most q customers before returning to a depot. We show that the stability condition in this case depends strongly on the geometry of the region. Several pol... The stochastic network calculus is an evolving new methodology for backlog and delay analysis of networks that can account for statistical multiplexing gain. This paper advances the stochastic network calculus by deriving a network service curve, which expresses the service given to a flow by the network as a whole in terms of a probabilistic bound. The presented network service curve permits the calculation of statistical end-to-end delay and backlog bounds for broad classes of arrival and service distributions. The benefits of the derived service curve are illustrated for the exponentially bounded burstiness (EBB) traffic model. It is shown that end-to-end performance measures computed with a network service curve are bounded by spl Oscr (H log H), where H is the number of nodes traversed by a flow. Using currently available techniques, which compute end-to-end bounds by adding single node results, the corresponding performance measures are bounded by spl Oscr (H sup 3 ). Many communication networks such as wireless networks only provide stochastic service guarantees. For analyzing stochastic service guarantees, research efforts have been made in the past few years to develop stochastic network calculus, a probabilistic version of (min, +) deterministic network calculus. However, many challenges have made the development difficult. Some of them are closely related to server modeling, which include output characterization, concatenation property, stochastic backlog guarantee, stochastic delay guarantee, and per-flow service under aggregation. In this paper, we propose a server model, called stochastic service curve to facilitate stochastic service guarantee analysis. We show that with the concept of stochastic service curve, these challenges can be well addressed. In addition, we introduce strict stochastic server to help find the stochastic service curve of a stochastic server, which characterizes the service of the server by two stochastic processes: an ideal service process and an impairment process.