resilience and efficiency of multi-layered networks

Presenter: Chang XINGSupervisor: Prof. Moshe ZUKERMANCo-supervisor: Prof. R. G. AddieDate: 13th December 2019

Resilience and Efficiency of Multi-layered Networks

IntroductionØ BackgroundØ Motivation

Outline1

Problem Definition2

Numerical Results3

Conclusion, Future Work and Q&A4

Ø Normal OperationØ Resilient DesignØ Validation and Verification

Ø Internet Traffic Pattern and Modelling Ø Network Layered TechnologiesØ Multi-layered Network Optimization

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Introduction Telecommunications Networks: Past, Present

1876

Telephone

1927Television

1983 Internet

Source: https://me.me/i/this-is-what-happens-in-an-internet-minute-facebook-yotu-e849a13f733945ecba547c42ca11118f

2019 Various services

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Fig. 1: Illustration of connectivity in a layered telecommunication network

Introduction Multi-layer, Multi-technology Network

Commonly deployed switching and transporting technologies include Internet Protocol (IP), Multi-Protocol Label Switching (MPLS), Ethernet, and Wavelength Division Multiplexing (WDM).

3Source: C. Xing, et. al., "Resource Provisioning for a Multi-layered Network," IEEE Access, vol. 7, no. 1, 2019.

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Motivation Challenges

Source: https://technology.ihs.com/572369/businesses-losing-700-billion-a-year-to-it-downtime-says-his;;https://www.nature.com/news/the-bandwidth-bottleneck-that-is-throttling-the-internet-1.20392.

Challenges for network design:♦ Multi-layered multi-technology growing architecture;; ♦ In addition to failures caused by nature, the complexity of the internet increases the risk of human-made failures.♦ Exponentially increasing traffic demands;;

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Motivation Aim

Aim: Provide a cost-effective and resilient resource provisioning for multi-layered networks• Design should be based on accurate traffic models;;• Dimension a layered structure considering different transmission technologies;;• Taking into account a range of possible failure scenarios;; • Develop a tool for efficient and scalable design algorithm;;

–We focus on network resource provisioning;; thus, the design of network protocols is not considered.

Problem Definition

Ø Internet Traffic Pattern and Modelling Ø Network Layered TechnologiesØ Multi-layer Network Optimization

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Traffic stream (statistical aggregation of flows) between a source and destination pair is modelled as eitherConstant Bit-Rate (CBR)a permanent demand which stays constant for its entire life-time.

orVariable Bit-Rate (VBR)transmit in bursts.

Internet Traffic Pattern and Modelling

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Internet Traffic Pattern and Modelling Poisson Pareto burst process (PPBP)

PPBP - Poisson arrival process of Pareto distributed bursts/flows

Long Range Dependent (LRD)is a widespread phenomenon has been found in

Ethernet traffic (Leland, et al. 94)VBR video traffic (Garrett & Whitt 94)Internet traffic (Paxson 95)MAN traffic (Addie, Zukerman, Neame 95)

Unlike voice traffic, data traffic is much more bursty with sizes ranging from extremely short (e.g. emails) to extremely long (e.g. video streaming).

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Ø Flow arrivals follow a Poisson process;;Ø Flow sizes are Pareto distributed;;Ø During a flow, bit rate is constant. All flows are CBR.

Internet Traffic Pattern and Modelling Pareto Distribution

,Pr 1, otherwise

x xX x

γ

δδ

−⎧⎛ ⎞ ≥⎪⎜ ⎟> = ⎨⎝ ⎠⎪⎩

The complementary distribution:

Fig. 2. Pareto distribution for different types of flow with δ = 3 and γ= 2.

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δ:The scale parameter representing theminimum value for random varible;γ: The shape paremeter of the Pareto distribution;δ >0;; γ >0.

Source: M. Zukerman, T. D. Neame and R. G. Addie, "Internet Traffic Modeling and Future Technology Implications,”Proceedings of INFOCOM 2003.

PPBP

Consider “Poisson Pareto burst process”

Mean

Variance

λ: Flow arrival rate;represents the aggratation of flows. 𝑟: Flow rate; is the same for all flows.

The PPBP model accurately exhibits LRD when γ <2.

Internet Traffic Pattern and Modelling

10Source: R. G. Addie, T. D. Neame, M. Zukerman, "Performance analysis of a Poisson–Pareto queue over the full range of system parameters," Computer Networks, vol. 53, no. 7, May 2009.

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Fig. 3 Layered architecture

v For simplicity, all the nodes havethe same structure, and includecapability to switch at all thelayers of the network;;

v Between each source anddestination nodes, multiple trafficstreams are assumed that aremodelled either by CBR or VBR;;

vModular capacity is assigned onlinks. Modular cost, modularcapacity in each layer, andtransmission cost in the physicallayer are given.

Model of a NetworkNetwork Layered Technologies

Source: V. Abramov,R. G. Addie, F. Li, Y. Peng and M. Zukerman, "A new teletraffic approach for network planning and evolution prediction," Proceedings of OFC/NFOEC 2012.

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ObjectivesMulti-layered Network Optimization

In the resilient optimization problem, we further maximize Earnings BeforeInterest and Taxes (EBIT) as the objective. EBIT is defined as annual revenuesminus annual costs. The costs include CAPEX, OPEX, and penalties(compensation to the customers when the service is interrupted or degraded).

In normal operations, the objective of our problem is to minimize the total network cost including capital expenditures (CAPEX) and operating expenses (OPEX).

Constraints

Commodity-flow conservation1. Flow conservationFor each node, the sum of the flows into the node must be equal to the sum of the flows out of the node;;2. Demand satisfactionThe overall traffic departing a source node or entering a destination node equals to traffic demand;;3. Capacity conservation The traffic load of a link is no larger than the capacity offered by that link. This depends on the blocking probability estimation function: Grade of Service (GoS) function.

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Multi-layered Network Optimization

Peak Rate Allocation (PRA)

For each connection, we allocate excusive capacity throughout its entire route based on its peak rate estimated by mean + 3 standard deviations.

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Multi-layered Network Optimization GoS Function

• For the normal distribution, thevalues less than three standarddeviations account for 99.7%.

• Capacity assignment based onmean + three standard deviationswill guarantee that 99.85% of thetraffic will not be blocked ordisrupted.

Source: https://zh.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7%E5%8E%9F%E5%89%87, Author: Dan Kernler 15

3-Sigma Rule

GoS Function Multi-layered Network Optimization

Fig. 4 The confidence intervals correspond to 3-sigma rule of the normal distribution.

Traffic is bursty: the peak rate of a traffic stream is much largerthan its average rate.Since not everyone will be using the network at the same time, wecan benefit by sharing its resources.

Statistical Multiplexing (SM)Sharing of network bandwidth between users according tothe statistics of their demand.

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Number of arrivals

Poisson Probability Function with λ= 10

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Source: slides for ICFCC 2010 by Prof. Moshe ZUKERMAN

Prob

abili

ty

Number of arrivals


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Prob

abili

ty

Number of arrivals


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Prob

abili

ty

As more VBR traffic streams are multiplexed together, thestandard deviation of the total traffic relative to its meanis reduced, so the gap between the PRA and the mean isalso reduced.This is called Statistical Multiplexing Gain.

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Multi-layered Network Optimization

Integer Linear Programming (ILP)

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1. Can use multiple-path routing;;2. ILP is limited to small networks;; 3. Can be used to validate heuristics, but still – only for small networks;;4. Limited to CBR traffic – this implies significant limitations, e.g.,

Cannot capture VBR traffic;; Cannot apply to dynamic connections;;

ILP heuristics could be applied to larger networks, however, they cannotget optimal solutions and are still limited to CBR traffic.

ILP formulations are used to minimize network cost in terms of port and transmission cost. ILP can find an optimal solution for capacity assignment.

Source: M. Pioro and D. Medhi, Routing, Flow, and Capacity Design inCommunication and Computer Networks. Morgan Kaufmann, Jul. 2004.

Heuristic Algorithm Multi-layer Market Algorithm (MMA)

MMA is implemented on a suitable user interface based on Network Markup Language (Netml).

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1. Fixed-point iterations: MMA iteratively assigns link capacities in each layer to meet the constraints.

2. Shortest path routing;; 3. Flexible virtual topology;;4. Scalable;; 5. Flow-size based routing;; 6. Dynamic links can be generated. Formed by setting up a path in the layer below whenever traffic requires it. A set-up cost is incurred for setting up a dynamic link.

MMA has been developed for ten years. Several papers and a thesis had been published about MMA.

q Considered CBR and PPBP traffic streams;; q Different traffic sharing allocation schemes were proposed;;q Some verifications for the results were provided.

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History of MMA

qDefine a rigorously formulation of non-linear multi-layered network optimization;;qImplement different traffic-sharing allocation schemes;;qImplement three type of links with new cost models;;qConsider network resilience;;qProvide comprehensive results for MMA validation based on various ILP benchmarks;;

qDemonstration based on extensive empirical results that MMA is solvable in polynomial time;;

qVerification for all the results in MMA by double-entry bookkeeping;;qUse of visualization for extra validation of MMA.

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Contribution of The Thesis

(In the last three years, approximately 10,000 lines of code were added to implement all these contributions in MMA.)

ØNormal OperationØResilient designØValidation and Verification

Numerical Results

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Six-Node Network Experiment (ILP vs MMA)

• Three layer are assumed;;• Between each source and destinationnode, six traffic streams are assumedthat are modelled either by CBR orVBR;;

• The proportion of VBR vs CBR traffichas been varied.

Fig. 5. Six-node network used for validation

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Six-Node Network Result (ILP vs MMA)

27Fig. 6 Cost Comparison of MMA vs. ILP

Under Normal Operation

v Same topology is used in all layers in ILP.


28Fig. 7 Comparison of MMA vs. ILP with fully-mesh topology in Layer 3

v The setup cost for dynamiclinks (DLs) is set to be zero.which encourages PPBPtraffic streams to use end-to-end DLs in Layer 3 toestimate the full potential interms of cost-saving ofdynamic links.

Six-Node Network Experiment (ILP vs MMA)

Fig. 8 Six-node network used for validation

The provision of resources for a network by guaranteed GoS (GGoS) with mean + three standard deviations under all the considered failures may be too expensive.

v GoS function can be relaxed and most services canstill operate with a lower but acceptable quality;;

v The link capacity also might be set to mean + twostandard deviations by degraded GoS (DGoS) or mean+ one standard deviation by extreme degraded GoS(EDGoS);;

v Further relaxing the GoS function, higher penalty willbe charged.

Resilient design

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v Same topology is used in all layers inILP.

v When we consider network resilience,we allocate the link capacity based onthe maximum capacity neededconsidering a range of single-linkfailures. The probability of the failuresis not considered.

Fig. 9 Cost comparison of MMA vs. ILP

Resilient Design


31Fig. 10 EBIT comparison of MMA vs. ILP

Resilient Design

• Since the resource allocation of thedegraded services is slightly lower inMMA+DGoS under failures, the un-affected links can serve more serviceswith the same amount of resources underfailure scenarios.

• When DLs are applied in resilient MMA,MMA+DL+GGoS gets optimal solutionssince the penalties involved in thedegraded services may significantlyinfluence the cost and EBIT.

32Fig. 11 Total network cost vs dynamic link set-up cost when all traffic are VBR

Total Network Cost vs. Dynamic Link Set-up Cost

This numerical study results in a polynomial complexity of O(|V|*|E|)+O(|V|^4) where O(|V|^4)is the dominant term. The goodness-to-fit measure R-square = 0.9976 for this polynomialfitting which indicates that the model fits the results very well.

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Running Time of MMA

Fig. 12. Running time in seconds of MMA on partially and fully meshed networks

Validation and Verification

Verification of MMADouble Entry Bookkeeping: widely used for error detection in the accounting field for centuries.

MMA computes a cost-per-packet for all traffic streams in thenetwork. These costs enable MMA to calculate the total networkcost by adding up these costs over all carried traffic. This shouldproduce the same estimate of total network cost as that obtained byadding up all the network resource costs associated with all links.

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Validation and Verification

Netml enables users to perceive and understand the assignment of capacity, routing, andutilization of resources. This highlights the importance of having visual tools, which enableusers to see into the details of the networks.

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To watch the demonstration, use the link:http://www.ee.cityu.edu.hk/~zukerman/netml2_Eng.mp4

Conclusion

1. Presented a multi-layer network optimization heuristic algorithm: MMA

l Traffic assumed to be mixture of CBR and VBR;; • Allow flow-size dependent routing;; • Consider EBIT model as the objective for resilient network design;;• Demonstrate that MMA is solvable in polynomial time.

2. Provide resilient and cost-effective resource provisioning considering statistical multiplexing, dynamic links, and different GoS functions.

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Future Work1. Apply the LRD model called Poisson Lomax Burst Process inMMA, which is modeled by Lomax distributed flows that arrive according to aPoisson process. As in Lomax, the minimum flow size can be zero, this allowsconsideration of many small flows in the model;;2. Extend the model to have flows from different sources that transmit atdifferent rates, e.g., to consider the flow rate r as a random variable;;3. Relaxing the assumption that all layers available in all nodes to considerpractical situations where some equipment is not available in some nodes;;4. Explore data in the real world to develop more realistic failure andEBIT models.

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Thank You For Listening!

Questions

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resilience and efficiency of multi-layered networks

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