On delay stable communications in asynchronous networks

This dissertation defines a frame forwarding technique offering a fixed delay to a subclass of traffic in closed industrial control networks. In these networks bandwidth is dedicated to periodic traffic supporting tight inter-process control and control loop communication. Ideally periodic traffic arrival will have minimal delay-jitter with constant realized delays. This simplifies the implementation of connected control devices. Furthermore networks are simplified with asynchronous node and switch operation. Switch designs are simplified as there is no dependence on adjacent switch clock operation. Correct network function only relies on switches directly traversed by each flow and is not dependent on complex clock synchronization mechanisms. Existing packet scheduling schemes that attempt to minimize delay-jitter, suffer from either requiring inter-switch clock coordination (i.e. RCSP-DJ), or maintain a fixed priority so that the highest priority flows must contend without regard to past frame arrival treatment (i.e. RCSP-RJ). In this dissertation the FlexTDMA protocol is defined which supports closed network communication. FlexTDMA will be enhanced to accommodate real-world networking conditions (FlexTDMA+) and will be enhanced to support simultaneous multicast (FlexTDMA++). The FlexTDMA scheduling algorithm delivers frame data on each flow nearly at the maximal delay bound with minimal delay-jitter in an asynchronous network. Industrial control switching network systems will benefit from FlexTDMA when the complexity of system level synchronization is unacceptable, but the component switches must operate independently. FlexTDMA does not require synchronous network clock coordination and preserves the data content of frames. FlexTDMA+ includes three improvements: baseline preemption, partial baselining and baseline deadline density control, which are used to support real-world conditions of node periodic on-off transmission, clock drift, frame loss and bandwidth load. FlexTDMA++ supports simultaneous multicast under real-world conditions of switch failures, node periodic on-off transmission, clock drift, frame loss and bandwidth load

Understanding Fairness and its Impact on Quality of Service in IEEE 802.11

The Distributed Coordination Function (DCF) aims at fair and efficient medium access in IEEE 802.11. In face of its success, it is remarkable that there is little consensus on the actual degree of fairness achieved, particularly bearing its impact on quality of service in mind. In this paper we provide an accurate model for the fairness of the DCF. Given M greedy stations we assume fairness if a tagged station contributes a share of 1/M to the overall number of packets transmitted. We derive the probability distribution of fairness deviations and support our analytical results by an extensive set of measurements. We find a closed-form expression for the improvement of long-term over short-term fairness. Regarding the random countdown values we quantify the significance of their distribution whereas we discover that fairness is largely insensitive to the distribution parameters. Based on our findings we view the DCF as emulating an ideal fair queuing system to quantify the deviations from a fair rate allocation. We deduce a stochastic service curve model for the DCF to predict packet delays in IEEE 802.11. We show how a station can estimate its fair bandwidth share from passive measurements of its traffic arrivals and departures

A generalized processor sharing approach to flow control in integrated services networks : the multiple node case

Caption title.Includes bibliographical references (p. 42).Supported by a Vinton Hayes Fellowship and a Center for Intelligent Control Systems Fellowship. Funded by the National Science Foundation. 8802991-NCR Funded by the Army Research Office. DAAL03-86-K-0171Abhay K. Parekh and Robert G. Gallager

Computation of a (min,+) multi-dimensional convolution for end-to-end performance analyzes

We investigate how to compute a (min,+) multi-dimensional convolution with application to the worst-case performance analyzes in "Pay Multiplexing Only Once" scenarios. In such scenarios, a flow encounters some cross-traffic along its path and each cross-traffic flow interfers over a connected subpath. When there is no cross-traffic, the analyzes boils down to classical (min,+) convolutions. We provide three proofs to a well-known lemma describing how to compute the convolution of piecewise affine convex functions

A study of multiplexing on to a variable-bit rate output channel in integrated-service networks.

by Chan-weng Lai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 81-[83]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Where May Soft Multiplexing Occur? --- p.2Chapter 1.1.1 --- Multiplexing VC's on to a VP --- p.2Chapter 1.1.2 --- Virtual Private Networks --- p.5Chapter 1.2 --- Survey of Previously Proposed Hard Multiplexing Schemes --- p.7Chapter 1.3 --- Contributions of This Thesis --- p.8Chapter 1.4 --- Organization of This Thesis --- p.10Chapter 2 --- "Effect of (δ,p) Channels in ATM Networks" --- p.12Chapter 2.1 --- Leaky Bucket --- p.13Chapter 2.2 --- "(δ, p) Channel" --- p.14Chapter 2.3 --- "Comparison of Deterministic VP's and(δ, p) VP's" --- p.17Chapter 2.4 --- A Simulation Study : The Effect of δ --- p.20Chapter 2.5 --- Summary of This Chapter --- p.23Chapter 3 --- Soft-Multiplexing Scheduling Schemes --- p.26Chapter 3.1 --- Issues in Soft Multiplexing --- p.27Chapter 3.2 --- First Come First Serve (FCFS) --- p.30Chapter 3.3 --- Fixed-resource Allocation --- p.32Chapter 3.4 --- Excess Token Passing --- p.35Chapter 3.5 --- Simulation Results --- p.38Chapter 3.6 --- Summary of This Chapter --- p.42Chapter 4 --- Analysis of Rate Proportional Token Passing --- p.44Chapter 4.1 --- The Fictitious System --- p.45Chapter 4.2 --- Leaky-Bucket-Controlled Sources --- p.49Chapter 4.3 --- Delay Bound for All Work Conserving Soft Multiplexers --- p.51Chapter 4.4 --- The All-Greedy Bound in a RPTP Multiplexer --- p.53Chapter 4.5 --- Calculation of the Worst-Case Delay in a RPTP Multiplexer --- p.56Chapter 4.6 --- Summary of This Chapter --- p.61Chapter 5 --- Implementation of RPTP --- p.63Chapter 5.1 --- Virtual Time Implementation --- p.64Chapter 5.2 --- Leaky Bucket Implementation --- p.70Chapter 5.3 --- Summary of This Chapter --- p.72Chapter 6 --- Conclusion --- p.73Chapter A --- End-to-end Delay/Backlog Bound in ATM Networks --- p.76Bibliography --- p.8

A measurement-based approach to service modeling and bandwidth estimation in IEEE 802.11 wireless networks

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Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation

Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson’s networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10%. Finally, we show the usefulness of the model for performing the dynamic provisioning of the vMME experimentally.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428)National research project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of Education, Culture and Sport (FPU Grant 13/04833). We would also like to thank the reviewers for their valuable feedback to enhance the quality and contribution of this wor

System identification of computer networks with random service

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Non-asymptotic performance evaluation and sampling-based parameter estimation for communication networks with long memory traffic

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