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On the Model Transform in Stochastic Network Calculus
"... Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical ..."
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Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical calculation. In theory, transform between different traffic arrival models and transform between different service models are possible. Nevertheless, the impact of the model transform on performance bounds has not been thoroughly investigated. This paper is to investigate the effect of the model transform and to provide practical guidance in the model selection in stochastic network calculus.
Stochastic Network Calculus for Performance Analysis of Internet Networks – An Overview and Outlook
"... Abstract—Stochastic network calculus is a theory for performance guarantee analysis of Internet networks. Originated in early 1990s, stochastic network calculus has its foundation on the minplus convolution and maxplus convolution queueing principles. Although challenging, it has shown tremendous ..."
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Abstract—Stochastic network calculus is a theory for performance guarantee analysis of Internet networks. Originated in early 1990s, stochastic network calculus has its foundation on the minplus convolution and maxplus convolution queueing principles. Although challenging, it has shown tremendous potential in dealing with queueing type problems encountered in Internet networks. By focusing on bounds, stochastic network calculus compliments the classical queueing theory. This paper provides an overview of stochastic network calculus from the queueing principle perspective and presents an outlook by discussing crucial yet still open challenges in the area. I.
Stochastic service curve and delay bound analysis: a single node case
 Computer Science from University of Kaiserslautern
, 2013
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Mathematical Formalisms for Performance Evaluation of NetworksonChip
"... This article reviews four popular mathematical formalisms—queueing theory, network calculus, schedulability analysis,anddataflow analysis—and how they have been applied to the analysis of onchip communication performance in SystemsonChip. The article discusses the basic concepts and results of ea ..."
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This article reviews four popular mathematical formalisms—queueing theory, network calculus, schedulability analysis,anddataflow analysis—and how they have been applied to the analysis of onchip communication performance in SystemsonChip. The article discusses the basic concepts and results of each formalism and provides examples of how they have been used in NetworksonChip (NoCs) performance analysis. Also, the respective strengths and weaknesses of each technique and its suitability for a specific purpose are investigated. An open research issue is a unified analytical model for a comprehensive performance evaluation of NoCs. To this end, this article reviews the attempts that have been made to bridge these formalisms.
A Node Operating Point Approach for Stochastic Analysis with Network Calculus
"... Abstract — The operating point of a node is an interesting concept from large deviations theory which defines the asymptotic buffer occupancy distribution at the node. The effective bandwidth function evolved out of large deviations theory, establishes the crucial connection between the node operati ..."
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Abstract — The operating point of a node is an interesting concept from large deviations theory which defines the asymptotic buffer occupancy distribution at the node. The effective bandwidth function evolved out of large deviations theory, establishes the crucial connection between the node operating point and the theory of statistical network calculus. This paper uses the concepts of effective bandwidth and effective capacity to describe independent stochastic arrival and service processes, respectively, and to identify node operating point to perform stochastic analysis with network calculus. The two main advantages of the approach used in this paper are: (i) the use of operating point to perform stochastic analysis provides insight into the queue dynamics, and (ii) the use of effective bandwidth and effective capacity functions within the framework of statistical network calculus allows efficient evaluation of performance bounds. Index Terms — network calculus, effective bandwidth, effective capacity, QoS, node operating point, large deviations theory I.
1 An Approach using NDemisupermartingales for the Stochastic Analysis of Networks
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A Software Defined Networking Architecture for the InternetofThings
"... has resulted in a number of widearea deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multihop adhoc and MANET routing protocols, they all must be effectively ..."
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has resulted in a number of widearea deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multihop adhoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a softwaredefined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (selfobserving and adapting via an embodied ObserveAnalyzeAdapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over tasklevel, multihop, and heterogeneous adhoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of widescale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities. I.
A Network Calculus Approach to Delay Evaluation of IEEE 802.11 DCF
"... Abstract—Stochastic network calculus is an evolving theory for network performance guarantee analysis. Although many theoretical results of this theory have been developed, there still lack applicable examples to demonstrate how it may be used. This paper exemplifies applying stochastic network calc ..."
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Abstract—Stochastic network calculus is an evolving theory for network performance guarantee analysis. Although many theoretical results of this theory have been developed, there still lack applicable examples to demonstrate how it may be used. This paper exemplifies applying stochastic network calculus to delay analysis of the IEEE 802.11 distributed coordination function (DCF). Analyzing and obtaining the stochastic characteristics of the single packet service time is the primary task. Then the stochastic behavior of the DCF is characterized by a timedomain server model, which describes the cumulative service time provided to an arrival flow using a probabilistic bound. Based on this server model, we obtain delay bounds for different arrival processes. In addition, the delay bounds also take buffer size into account. The analytical bounds are further discussed using numerical results. Through these, we present a stochastic network calculus approach to delay evaluation of the DCF. Index Terms—Stochastic network calculus, IEEE 802.11 DCF, Stochastic service curve, Delay bound