Analysis of a Real-Time Network Using Statistical Network Calculus with Approximate Invariance of Effective Bandwidth

Angrishi, Kishore; Zhang, Shu; Killat, Ulrich

doi:10.1007/978-3-540-69962-0_16

Cited by 8 publications

(13 citation statements)

References 15 publications

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“…• EC is the dual concept of effective bandwidth [8], [9] and shows the maximum constant arrival rate for a wireless channel while satisfying a delay outage probability constraint [10]. This feature can be exploited to achieve the required QoS for some applications with specific QoS requirements.…”

Section: A Motivation: Need Of Effective Capacity Mathematical Modelmentioning

confidence: 99%

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

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Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physicallayer channel models, however, do not explicitly consider qualityof-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

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Section: A Motivation: Need Of Effective Capacity Mathematical Modelmentioning

confidence: 99%

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

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“…In [2], authors used moment generating function of the arrival traffic and conjugate moment generating function of the service process to derive the performance measures in a network. Here we adopt the popular notions of effective bandwidth (α h ) [3] and effective capacity (β h ) [4,5,6] from large deviations theory to describe the stochastic arrival process and the service process characterizing the service offered at a network node h, respectively. The effective bandwidth of an arrival traffic A h from [3], for all θ, t > 0, is given as…”

Section: Arrival and Service Modelsmentioning

confidence: 99%

“…Effective bandwidth describes the minimum bandwidth required at a network node to provide an expected QoS for a given traffic. Similarly, the concept of effective capacity [4,5,6] from large deviations theory can be used to represent the stochastic service received by an arrival traffic at a network node. The QoS measures at a network node can be expressed using the large deviations theory under many sources limiting regime (infinite sources) in terms of effective capacity and effective bandwidth.…”

Section: Introductionmentioning

confidence: 99%

“…The key contribution of this paper is that we develop an end-to-end stochastic network calculus based on the notions of effective bandwidth [3] and effective capacity [4,5,6] from large deviations theory. Such an extension of end-to-end stochastic network calculus allows the stochastic information about the arrival traffic and the service available to an arrival traffic at a network node to be retained as long as possible using the concept of effective bandwidth and effective capacity, respectively, which in turn enables efficient computation of end-to-end stochastic delay and backlog bounds than in other approaches with statistical envelopes [7,8,9,10] where the stochastic information about the arrival and the service processes are lost as soon as statistical envelopes are fixed.…”

Section: Introductionmentioning

confidence: 99%

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An end-to-end stochastic network calculus with effective bandwidth and effective capacity

Angrishi¹

2013

Computer Networks

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Network calculus is an elegant theory which uses envelopes to determine the worst-case performance bounds in a network. Statistical network calculus is the probabilistic version of network calculus, which strives to retain the simplicity of envelope approach from network calculus and use the arguments of statistical multiplexing to determine probabilistic performance bounds in a network. The tightness of the determined probabilistic bounds depends on the efficiency of modelling stochastic properties of the arrival traffic and the service available to the traffic at a network node. The notion of effective bandwidth from large deviations theory is a well known statistical descriptor of arrival traffic. Similarly, the notion of effective capacity summarizes the time varying resource availability to the arrival traffic at a network node. The main contribution of this paper is to establish an end-to-end stochastic network calculus with the notions of effective bandwidth and effective capacity which provides efficient end-to-end delay and backlog bounds that grows linearly in the number of nodes (H) traversed by the arrival traffic, under the assumption of independence.

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“…Van der Auwera in [1] examines the implications of video traffic smoothing on statistically multiplexed video streams, focused on identifying the levels of smoothing that give (bufferless) statistical multiplexing performance close to an optimal off-line smoothing technique. Angrishi in [2] uses the large deviations theory to express the QoS measure at the link in terms of effective capacity. Li, Huang and Devetsikiotis in [3] work on overcoming the conservative nature of effective bandwidth by using a robust adaptive effective bandwidth allocation algorithm using statistical multiplexing gain occurring among multiple traffic classes.…”

Section: Introductionmentioning

confidence: 99%

Statistical Multiplexing of Homogeneous Streams results in Linear Bandwidth Gains

Anjum

2012

JSEA

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Statistical multiplexing of traffic streams results in reduced network bandwidth requirement. The resulting gain increases with the increase in the number of streams being multiplexed together. However, the exact shape of the gain curve, as more and more streams are multiplexed together, is not known. In this paper, we first present the generalized result that the statistical gain of combining homogeneous traffic streams, of any traffic type, is a linear function of the number of streams being multiplexed. That is, given a fixed Quality of Service (QoS) constraint, like percentile delay, D, the bandwidth requirement of n streams to satisfy the delay constraint D is n x R x c where R is the bandwidth requirement of a single stream that satisfies the constraint D and c ε (0,1]. We present the linear bandwidth gain result, using an extensive simulation study for video traces, specifically, streaming video (IPTV traces) and interactive video (CISCO Telepresence traces). The linear bandwidth gain result is then verified using analytical tools from two different domains. First, we validate the linearity using Queueing Theory Analysis, specifically using Interrupted Poisson Process (IPP) and Markov Modulated Poisson Process (MMPP) modeling. Second, we formally prove the linear behavior using the Asymptotic Analysis of Algorithms, specifically, the Big-O analysis.

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Analysis of a Real-Time Network Using Statistical Network Calculus with Approximate Invariance of Effective Bandwidth

Cited by 8 publications

References 15 publications

Effective Capacity in Wireless Networks: A Comprehensive Survey

Effective Capacity in Wireless Networks: A Comprehensive Survey

An end-to-end stochastic network calculus with effective bandwidth and effective capacity

Statistical Multiplexing of Homogeneous Streams results in Linear Bandwidth Gains

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