Results 1  10
of
35
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 ..."
Abstract

Cited by 28 (2 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 28 (2 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 25 (15 self)
 Add to MetaCart
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).
Analysis of stochastic service guarantees in communication networks: A server model
 In Proc. of the International Workshop on Quality of Service (IWQoS 2005
, 2005
"... Abstract. The arrival curve has been used as a powerful concept for deterministic service guarantee analysis in communication networks. Since many applications and networks do not require or provide deterministic service guarantees, stochastic service guarantee analysis is becoming increasingly impo ..."
Abstract

Cited by 20 (8 self)
 Add to MetaCart
Abstract. The arrival curve has been used as a powerful concept for deterministic service guarantee analysis in communication networks. Since many applications and networks do not require or provide deterministic service guarantees, stochastic service guarantee analysis is becoming increasingly important and has attracted a lot of research attention in recent years. For this, several probabilistic versions of the arrival curve have been proposed in the literature. They extend the concept of arrival curve to the stochastic case based on its traffic amount property. In this paper, we explore another property, called the virtual backlog property, of an arrival curve. Based on the virtual backlog property, we introduce the concept of virtualbacklogcentric (v.b.c) stochastic arrival curve to facilitate the analysis of stochastic service guarantees. We prove that a v.b.c stochastic arrival curve has a similar duality as a (deterministic) arrival curve. With the concept of v.b.c stochastic arrival curve, we derive results for stochastic service guarantee analysis of systems with the timevarying setting. In addition, we prove that many wellknown types of traffic can be readily represented using v.b.c stochastic arrival curves.
A minplus calculus for endtoend statistical service guarantees
 IEEE TRANSACTION ON INFORMATION THEORY
, 2006
"... The network calculus offers an elegant framework for determining worstcase bounds on delay and backlog in a network. This paper extends the network calculus to a probabilistic framework with statistical service guarantees. The notion of a statistical service curve is presented as a probabilistic b ..."
Abstract

Cited by 20 (6 self)
 Add to MetaCart
The network calculus offers an elegant framework for determining worstcase bounds on delay and backlog in a network. This paper extends the network calculus to a probabilistic framework with statistical service guarantees. The notion of a statistical service curve is presented as a probabilistic bound on the service received by an individual flow or an aggregate of flows. The problem of concatenating pernode statistical service curves to form an endtoend (network) statistical service curve is explored. Two solution approaches are presented that can each yield statistical network service curves. The first approach requires the availability of time scale bounds at which arrivals and departures at each node are correlated. The second approach considers a service curve that describes service over time intervals. Although the latter description of service is less general, it is argued that many practically relevant service curves may be compliant to this description.
Network calculus delay bounds in queueing networks with exact solutions
 In 20th International Teletraffic Congress (ITC
, 2007
"... Abstract. The purpose of this paper is to shed light on the accuracy of probabilistic delay bounds obtained with network calculus. In particular, by comparing calculus bounds with exact results in a series of M/M/1 queues with cross traffic, we show that reasonably accurate bounds are achieved when ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
Abstract. The purpose of this paper is to shed light on the accuracy of probabilistic delay bounds obtained with network calculus. In particular, by comparing calculus bounds with exact results in a series of M/M/1 queues with cross traffic, we show that reasonably accurate bounds are achieved when the percentage of cross traffic is low. We use recent results in network calculus and, in addition, propose novel bounds based on Doob’s maximal inequality for supermartingales. In the case of single M/M/1 and �when M/D/1 queues, our results improve existing bounds by the utilization factor ρ converges to one. 1 Ω�log(1−ρ) −1
On Theta (H log H) scaling of network delays
 in Proc. of IEEE INFOCOM
"... Abstract — A recent result in network calculus theory provided statistical delay bounds for exponentially bounded traffic that grow as O(H log H) with the number of nodes on the network path. 1 In this paper we establish the corresponding lower bound which shows that under these assumptions, typical ..."
Abstract

Cited by 7 (6 self)
 Add to MetaCart
Abstract — A recent result in network calculus theory provided statistical delay bounds for exponentially bounded traffic that grow as O(H log H) with the number of nodes on the network path. 1 In this paper we establish the corresponding lower bound which shows that under these assumptions, typical endtoend delays can indeed grow as Θ (H log H). The lower bound is obtained by analyzing the endtoend delay in a tandem network. A critical assumption is that each packet maintains the same service time at each traversed node. The results of this paper provide conclusive evidence that, in general, delays have a qualitatively different scaling behavior than is suggested by a worstcase analysis or by assuming independence on the service obtained at network nodes. I.
Network calculus and queueing theory: two sides of one coin
 in Proc. 4th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS
, 2009
"... Network calculus is a theory dealing with queueing type problems encountered in computer networks, with particular focus on quality of service guarantee analysis. Queueing theory is the mathematical study of queues, proven to be applicable to a wide area of problems, generally concerning about the ( ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
Network calculus is a theory dealing with queueing type problems encountered in computer networks, with particular focus on quality of service guarantee analysis. Queueing theory is the mathematical study of queues, proven to be applicable to a wide area of problems, generally concerning about the (average) quantities in an equilibrium state. Since both network calculus and queueing theory are analytical tools for studying queues, a question arises naturally as is if and where network calculus and queueing theory meet. In this paper, we explore queueing principles that underlie network calculus and exemplify their use. Particularly, based on the network calculus queueing principles, we show that for GI/GI/1, similar inequalities in the theory of queues can be derived. In addition, we prove that the endtoend performance of a tandem network is independent of the order of servers in the network even under some general settings. Through these, we present a network calculus perspective on queues and relate network calculus to queueing theory. 1.
Stochastic service guarantee analysis based on timedomain models
 In Proc. MASCOTS
, 2009
"... Abstract—Stochastic network calculus is a theory for stochastic service guarantee analysis of computer communication networks. In the current stochastic network calculus literature, its traffic and server models are typically based on the cumulative amount of traffic and cumulative amount of service ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
Abstract—Stochastic network calculus is a theory for stochastic service guarantee analysis of computer communication networks. In the current stochastic network calculus literature, its traffic and server models are typically based on the cumulative amount of traffic and cumulative amount of service respectively. However, there are network scenarios where the applicability of such models is limited, and hence new ways of modeling traffic and service are needed to address this limitation. This paper presents timedomain models and results for stochastic network calculus. Particularly, we define traffic models, which are based on probabilistic lowerbounds on cumulative packet interarrival time, and server models, which are based on probabilistic upperbounds on cumulative packet service time. In addition, examples demonstrating the use of the proposed timedomain models are provided. On the basis of the proposed models, the five basic properties of stochastic network calculus are also proved, which implies broad applicability of the proposed timedomain approach. I.