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236
Load Balanced Birkhoffvon Neumann Switches, Part II: Multistage Buffering
, 2001
"... The main objective of this sequel is to solve the outofsequence problem that occurs in the load balanced Birkhoffvon Neumann switch with onestage buffering. We do this by adding a loadbalancing buffer in front of the first stage and a resequencingandoutput buffer after the second stage. Moreo ..."
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Cited by 115 (13 self)
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The main objective of this sequel is to solve the outofsequence problem that occurs in the load balanced Birkhoffvon Neumann switch with onestage buffering. We do this by adding a loadbalancing buffer in front of the first stage and a resequencingandoutput buffer after the second stage. Moreover, packets are distributed at the first stage according to their flows, instead of their arrival times in Part I. In this paper, we consider multicasting ows with two types of scheduling policies: the First Come First Served (FCFS) policy and the Earliest Deadline First (EDF) policy. The FCFS policy requires a jitter control mechanism in front of the second stage to ensure proper ordering of the traffic entering the second stage. For the EDF scheme, there is no need for jitter control. It uses the departure times of the corresponding FCFS outputbuffered switch as deadlines and schedules packets according to their deadlines. For both policies, we show that the endtoend delay through our multistage switch is bounded above by the sum of the delay from the corresponding FCFS outputbuffered switch and a constant that only depends on the size of the switch and the number of multicasting flows supported by the switch.
Stochastic Network Calculus
, 2008
"... A basic calculus is presented for stochastic service guarantee analysis in communication networks. Central to the calculus are two definitions, maximum(virtual)backlogcentric (m.b.c) stochastic arrival curve and stochastic service curve, which respectively generalize arrival curve and service c ..."
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Cited by 64 (18 self)
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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)backlogcentric (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) perflow 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.
Theories and Models for Internet Quality of Service
, 2002
"... We survey recent advances in theories and models for Internet Quality of Service (QoS). We start with the theory of network calculus, which lays the foundation for support of deterministic performance guarantees in networks, and illustrate its applications to integrated services, differentiated serv ..."
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Cited by 55 (1 self)
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We survey recent advances in theories and models for Internet Quality of Service (QoS). We start with the theory of network calculus, which lays the foundation for support of deterministic performance guarantees in networks, and illustrate its applications to integrated services, differentiated services, and streaming media playback delays. We also present mechanisms and architecture for scalable support of guaranteed services in the Internet, based on the concept of a stateless core. Methods for scalable control operations are also briefly discussed. We then turn our attention to statistical performance guarantees, and describe several new probabilistic results that can be used for a statistical dimensioning of differentiated services. Lastly, we review recent proposals and results in supporting performance guarantees in a best effort context. These include models for elastic throughput guarantees based on TCP performance modeling, techniques for some quality of service differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support.
Maintaining Packet Order in TwoStage Switches
, 2002
"... High performance packet switches frequently use a centralized scheduler (also known as an arbiter) to determine the configuration of a nonblocking crossbar. The scheduler often limits the scalability of the system because of the frequency and complexity of its decisions. A recent paper by C.S. Cha ..."
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Cited by 46 (7 self)
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High performance packet switches frequently use a centralized scheduler (also known as an arbiter) to determine the configuration of a nonblocking crossbar. The scheduler often limits the scalability of the system because of the frequency and complexity of its decisions. A recent paper by C.S. Chang et al. introduces an interesting twostage switch, in which each stage uses a trivial deterministic sequence of configurations. The switch is simple to implement at high speed and has been proved to provide 100% throughput for a broad class of traffic. Furthermore, there is a bound between the average delay of the twostage switch and that of an ideal outputqueued switch. However, in its simplest form, the switch missequences packets by an arbitrary amount. In this paper, building on the twostage switch, we present an algorithm called Full Frames First (FFF), that prevents missequencing while maintaining the performance benefits (in terms of throughput and delay) of the basic twostage switch. FFF comes at some additional cost, which we evaluate in this paper.
A Calculus for Endtoend Statistical Service Guarantees
, 2001
"... The deterministic network calculus offers an elegant framework for determining delays and backlog in a network with deterministic service guarantees to individual traffic flows. A drawback of the deterministic network calculus is that it only provides worstcase bounds. Here we present a network cal ..."
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Cited by 42 (8 self)
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The deterministic network calculus offers an elegant framework for determining delays and backlog in a network with deterministic service guarantees to individual traffic flows. A drawback of the deterministic network calculus is that it only provides worstcase bounds. Here we present a network calculus for statistical service guarantees, which can exploit the statistical multiplexing gain of sources. We introduce the notion of an effective service curve as a probabilistic bound on the service received by an individual flw, and construct an effective service curve for a network where capacities are provisioned exclusively to aggregates of flows. Numerical examples demonstrate that the calculus is able to extract a significant amount of multiplexing gain in networks with a large number of flows.
A Framework for Guaranteeing Statistical QoS
, 2001
"... Continuousmedia traffic (i.e., audio and video) can tolerate some loss but has rigid delay constraints. A natural QoS requirement for a continuousmedia connection is a prescribed limit on the fraction of traffic that exceeds an endtoend delay constraint. We propose and analyze a framework that p ..."
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Cited by 41 (1 self)
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Continuousmedia traffic (i.e., audio and video) can tolerate some loss but has rigid delay constraints. A natural QoS requirement for a continuousmedia connection is a prescribed limit on the fraction of traffic that exceeds an endtoend delay constraint. We propose and analyze a framework that provides such a statistical QoS guarantee to traffic in a packetswitched network. Providing statistical guarantees in a network is a notoriously difficult problem because traffic flows lose their original statistical characterizations at the outputs of queues. Our scheme uses bufferless statistical multiplexing combined with cascaded leakybuckets for smoothing and traffic contracting. This scheme along with a novel method for bounding the loss probability gives a tractable framework for providing endtoend statistical QoS. Using MPE(] video traces, we present numerical resuits that compare the connectioncarrying capacity of our scheme with that of guaranteed service schemes (i.e., no loss) using (]PS and RCS. Our numerical work indicates that our scheme can support significantly more connections without introducing significant traffic loss.
A network service curve approach for the stochastic analysis of networks
 IN PROCEEDINGS OF ACM SIGMETRICS
, 2005
"... 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 ne ..."
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Cited by 37 (2 self)
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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 endtoend 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 endtoend performance measures computed with a network service curve are bounded by O (H log H), where H is the number of nodes traversed by a flow. Using currently available techniques that compute endtoend bounds by adding single node results, the corresponding performance measures are bounded by O(H³).
An endtoend probabilistic network calculus with moment generating functions
 in Proc. IEEE 14th International Workshop on Quality of Servic (IWQoS
"... Abstract — Network calculus is a minplus system theory for performance evaluation of queuing networks. Its elegance stems from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worstcase assumptions of network calculus to develop a probabi ..."
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Cited by 34 (2 self)
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Abstract — Network calculus is a minplus system theory for performance evaluation of queuing networks. Its elegance stems from intuitive convolution formulas for concatenation of deterministic servers. Recent research dispenses with the worstcase assumptions of network calculus to develop a probabilistic equivalent that benefits from statistical multiplexing. Significant achievements have been made, owing for example to the theory of effective bandwidths, however, the outstanding scalability set up by concatenation of deterministic servers has not been shown. This paper establishes a concise, probabilistic network calculus with moment generating functions. The presented work features closedform, endtoend, probabilistic performance bounds that achieve the objective of scaling linearly in the number of servers in series. The consistent application of moment generating functions put forth in this paper utilizes independence beyond the scope of current statistical multiplexing of flows. A relevant additional gain is demonstrated for tandem servers with independent crosstraffic. I.
Scaling Properties of Statistical Endtoend Bounds in the Network Calculus
"... 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 n ..."
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Cited by 29 (17 self)
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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 endtoend 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 endtoend performance measures computed with a network service curve are bounded by O (H log H), where H is the number of nodes traversed by a flow. Using currently available techniques, which compute endtoend bounds by adding single node results, the corresponding performance measures are bounded by O (H³).
On the Performance of Multiplexing Independent Regulated Inputs
, 2001
"... In this paper, we consider the performance analysis problem for a work conserving link with a large number of independent regulated inputs. For such a problem, we derive simple stochastic bounds under a general trac constraint for the inputs. The bound for queue length is shown to be a stochastic ex ..."
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Cited by 29 (0 self)
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In this paper, we consider the performance analysis problem for a work conserving link with a large number of independent regulated inputs. For such a problem, we derive simple stochastic bounds under a general trac constraint for the inputs. The bound for queue length is shown to be a stochastic extension of the deterministic worst case bound and it is asymptotically tighter than the bound in Kesidis and Konstantopoulos [23]. We also test the bound by considering periodic inputs with independent starting phases. Based on Sanov's theorem and importance sampling, we propose a fast simulation algorithm that achieves signi cant variance reduction. The simulations results are compared with our stochastic bound and the bound in [23].