E E 681 - Module 18 M.H. Clouqueur and W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 Analysis of Path Availability in Span-Restorable.

Slides:



Advertisements
Similar presentations
February 20, Spatio-Temporal Bandwidth Reuse: A Centralized Scheduling Mechanism for Wireless Mesh Networks Mahbub Alam Prof. Choong Seon Hong.
Advertisements

Impact of Interference on Multi-hop Wireless Network Performance Kamal Jain, Jitu Padhye, Venkat Padmanabhan and Lili Qiu Microsoft Research Redmond.
Lecture 4. Topics covered in last lecture Multistage Switching (Clos Network) Architecture of Clos Network Routing in Clos Network Blocking Rearranging.
Copyright © Wayne D. Grover 2000 EE 681 Fall 2000 Lecture 14 Mesh-restorable Network Design (1) Wayne D. Grover, TRLabs / University of Alberta October.
Order Statistics Sorted
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
E E Module 17 W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 ATM VP-based (or MPLS path) Restoration with Controlled Over-
W.D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 Mesh-restorable Network Design (2) E E Module 13.
Markov Method of Availability Analysis, APS systems, and Availability Simulation Methods E E 681 Module 21 W. D. Grover TRLabs & University of Alberta.
Jan 13, 2006Lahore University of Management Sciences1 Protection Routing in an MPLS Network using Bandwidth Sharing with Primary Paths Zartash Afzal Uzmi.
Capacity Design Studies of Span-Restorable Mesh Transport Networks With Shared-Risk Link Group (SRLG) Effects John Doucette, Wayne D. Grover
P-Cycle Network Design: from Fewest in Number to Smallest in Size Diane P. OnguetouWayne D. Grover Diane P. Onguetou and Wayne D. Grover TRLabs and ECE,
Quantitative Comparison of End-to-End Availability of Service Paths in Ring and Mesh- Restorable Networks Matthieu Clouqueur, Wayne D. Grover
Mesh Restorable Networks with Complete Dual Failure Restorability and with Selectvely Enhanced Dual-Failure Restorability Properties Matthieu Clouqueur,
Restoration Routing in MPLS Networks Zartash Afzal Uzmi Computer Science and Engineering Lahore University of Management Sciences.
BROADNETS 2004 San José, California, USA October 25-29, 2004 p-Cycle Network Design with Hop Limits and Circumference Limits Adil Kodian, Anthony Sack,
Detecting Network Intrusions via Sampling : A Game Theoretic Approach Presented By: Matt Vidal Murali Kodialam T.V. Lakshman July 22, 2003 Bell Labs, Lucent.
December 20, 2004MPLS: TE and Restoration1 MPLS: Traffic Engineering and Restoration Routing Zartash Afzal Uzmi Computer Science and Engineering Lahore.
Effects of TSI/TSA (or Wavelength Conversion) on Ring Loading E E Module 8 W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003.
Models for Measuring and Hedging Risks in a Network Plan
Exploiting Forcer Structure to Serve Uncertain Demands and Minimize Redundancy of p-Cycle Networks Gangxiang Shen & Wayne D. Grover TRLabs and University.
Mesh Restorable Networks with Multiple Quality of Protection Classes Wayne D. Grover, Matthieu Clouqueur TRLabs and.
High availability survivable networks Wayne D. Grover, Anthony Sack 9 October 2007 High Availability Survivable Networks: When is Reducing MTTR Better.
High-Availability Network Architectures (HAVANA): High-Availability Network Architectures (HAVANA): Comparative Study of Fully Pre-Cross- Connected Protection.
EE 685 presentation Optimization Flow Control, I: Basic Algorithm and Convergence By Steven Low and David Lapsley Asynchronous Distributed Algorithm Proof.
Span-restorable Mesh Network Design (1) W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 E E Module 11 ( Version for book.
Path Protection in MPLS Networks Using Segment Based Approach.
Routing algorithms, all distinct routes, ksp, max-flow, and network flow LPs W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 E.
OFC 2004, Los Angeles, CA Restorable Mesh Network Design under Demand Uncertainty: Toward “Future Proofed” Transport Investments Dion Leung, Wayne Grover.
Capacity Requirements for Network Recovery from Node Failure with Dynamic Path Restoration Gangxiang Shen and Wayne D. Grover TRLabs and University of.
Estimation 8.
University of Alberta ECE Department Network Systems Gangxiang Shen, Wayne D. Grover Extending the p-Cycle Concept to Path-Segment Protection Gangxiang.
© Rui Wang Cycle-Oriented Distributed Preconfiguration Ring-like Speed with Mesh-like Capacity for Self-planning Network Restoration 1 Sep Rui Wang.
Advances in Optical Network Design with p-Cycles: Joint optimization and pre-selection of candidate p-cycles (work in progress) Wayne D. Grover, John Doucette.
Airline Schedule Optimization (Fleet Assignment II) Saba Neyshabouri.
A Combined Effort of OptiCal & LUMOS NETWORKS EE 290Q Lukas Chrostowski, Carlos Mateus, Fan Mo, Lixia Zhou.
Topology Design for Service Overlay Networks with Bandwidth Guarantees Sibelius Vieira* Jorg Liebeherr** *Department of Computer Science Catholic University.
Copyright © Wayne D. Grover 2000 EE 681 Fall 2000 Lecture 15 Mesh-restorable Network Design (2) W. D. Grover, October 26, 2000 copyright © Wayne D. Grover.
NOBEL WP5 Meeting Munich – 14 June 2005 WP5 Cost Study Group Author:Martin Wade (BT) Lead:Andrew Lord (BT) Relative Cost Analysis of Transparent & Opaque.
Copyright © Cengage Learning. All rights reserved. 5 Probability Distributions (Discrete Variables)
CMSC 345 Fall 2000 Unit Testing. The testing process.
Network Aware Resource Allocation in Distributed Clouds.
Background on Reliability and Availability Slides prepared by Wayne D. Grover and Matthieu Clouqueur TRLabs & University of Alberta © Wayne D. Grover 2002,
Higashino Lab. Maximizing User Gain in Multi-flow Multicast Streaming on Overlay Networks Y.Nakamura, H.Yamaguchi and T.Higashino Graduate School of Information.
Researchers: Preet Bola Mike Earnest Kevin Varela-O’Hara Han Zou Advisor: Walter Rusin Data Storage Networks.
E E 681 Fall Lecture 23 Course Wrap-up & Challenges for Future Optical Networking Matthieu Clouqueur TR Labs & University of Alberta.
Optimization Flow Control—I: Basic Algorithm and Convergence Present : Li-der.
Optimal Content Delivery with Network Coding Derek Leong, Tracey Ho California Institute of Technology Rebecca Cathey BAE Systems CISS 2009 March 19, 2009.
The Application of The Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem International Conference on Future Computer and Communication,
Copyright © Cengage Learning. All rights reserved. 5 Probability Distributions (Discrete Variables)
DISTRIBUTION AND NETWORK MODELS (1/2)
EE 685 presentation Optimization Flow Control, I: Basic Algorithm and Convergence By Steven Low and David Lapsley.
Impact of OXC Failures on Network Reliability Zsolt Pándi a, Andrea Fumagalli a, Marco Tacca a, Lena Wosinska b a OpNeAR Lab., Erik Jonsson Shool of EECS,
1 P-Cycles. 2 What’s a p-Cycle? A preconfigured cycle formed out of the spare capacities in the network –A p-cycle uses one unit of spare capacity on.
Community Detection Algorithms: A Comparative Analysis Authors: A. Lancichinetti and S. Fortunato Presented by: Ravi Tiwari.
A Comparative Study of the DNS Design with DHT-Based Alternatives 95/08/31 Chen Chih-Ming.
Tunable QoS-Aware Network Survivability Presenter : Yen Fen Kao Advisor : Yeong Sung Lin 2013 Proceedings IEEE INFOCOM.
An Exact Algorithm for Difficult Detailed Routing Problems Kolja Sulimma Wolfgang Kunz J. W.-Goethe Universität Frankfurt.
Network Dynamics and Simulation Science Laboratory Structural Analysis of Electrical Networks Jiangzhuo Chen Joint work with Karla Atkins, V. S. Anil Kumar,
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
Chapter 15 Running Time Analysis. Topics Orders of Magnitude and Big-Oh Notation Running Time Analysis of Algorithms –Counting Statements –Evaluating.
Joint Routing and Scheduling Optimization in Wireless Mesh Networks with Directional Antennas A. Capone, I. Filippini, F. Martignon IEEE international.
Architecture and Algorithms for an IEEE 802
John Doucette and Wayne D. Grover
Globecom 2003 December 1-5, San Francisco, California
Introduction to Operations Research
Network Survivability
Data and Computer Communications
TRLabs & University of Alberta © Wayne D. Grover 2002, 2003, 2004
Span-restorable Mesh Network Design
Presentation transcript:

E E Module 18 M.H. Clouqueur and W. D. Grover TRLabs & University of Alberta © Wayne D. Grover 2002, 2003 Analysis of Path Availability in Span-Restorable Mesh Networks

E E Module 18 © Wayne D. Grover 2002, Review of Mesh Design Motivation: Something must be done to reduce the impact of network element failures on service availability Solution: Mesh Restoration Mechanism (Requires extra capacity) Capacity planning methods: Max Latching Herzberg Modular capacity placement Joint working-spare capacity placement More and more capacity efficient but Availability ??? Availability Capacity Intuitively: Questions to answer: How much does mesh restoration improve the availability of service? How does the availability depend on the total capacity?

E E Module 18 © Wayne D. Grover 2002, What Causes Unavailability? Single span failures Multiple span failures Node failures Span maintenance services Combinations of the above What we need to compare : Which are the most important? Number of such events Example: Probability of bringing the system under study in down state By doing this comparison the major contributor to unavailability appears to be: Combination of Span failure and Span maintenance service (equivalent to dual span failure in the worst case)

E E Module 18 © Wayne D. Grover 2002, Impact of Failures For the previous comparison we could only guess or make assumptions for the value of the impact of each failure categories. Examples: Single span failure, Impact = 0 (network fully restorable to single failures) Dual span failure, Impact = 0.5 (at least half of the traffic on average should be restorable) Determination of availability of service paths: We need to know the exact value of the impact of each failure scenario on the availability of that service path Availability analysis of path p: Failure of (S 1, S 2 )  Impact = Failure of (S 1, S 3 )  Impact = Failure of (S i, S j )  Impact = We need a tool that determines the probability of path p being down for any given set of failed spans...

E E Module 18 © Wayne D. Grover 2002, Problem of Independence of Span Failures The contribution of a failure event to the unavailability is: For a dual span failure: Based on the assumption that failures of S i and S j are independent Special Case: S1S1 S2S2 S3S3 S2S2 S1S1 In that case: This span does not really exist but rather Common cable sheaths

E E Module 18 © Wayne D. Grover 2002, Path Availability Calculation Exact Expression: Simplified approach: Where: U * links, i is the equivalent link unavailability on span i Advantage: we only need to compute one value for each span and then use those values for the calculation of end-to-end availability. Drawback of simplified approach: Some failure events contribute to the unavailability of links on several spans in a neighbourhood and can therefore be counted several times when summing the U * links,i ’s.

E E Module 18 © Wayne D. Grover 2002, Link Equivalent Unavailability Concept of Equivalent Unavailability: Non-restorable network: U link = U span (physical unavailability)  When the span is down, the link is down Restorable network: U link = U link * (Equivalent link unavailability) U link * is different from U span because of the restoration mechanism We will see that U link * is in the order of U span 2 therefore U link * << U span

E E Module 18 © Wayne D. Grover 2002, Derivation of U link * It can easily be shown that the expected number of failed and non restored links in the network at any time is: R 2 : Average Dual Failure Restorability of Links In general U links,i * can be defined as: The only unknown S: Total number of spans Us: Average physical unavailability of spans w: Average working capacity of spans Span-specific average U * link (i) can be obtained using span-specific average R 2: R 2 (i) (calculated over S-1 dual-failure scenarios involving span i) Nab: non restored working units in the case of failure of span a and span b

E E Module 18 © Wayne D. Grover 2002, Determination of R 2 There is no closed form model for R2 as the impact of each failure scenario depends on several factors specific to the failure case. However failures events can be divided into a few main categories: Case 0: Span failure and w i > feasible spare paths Case 1: Two failures but no spatial interactions Case 2: Two failures and spatial interactions (competition for spare capacity) Case 3: Two failures with second failure hitting the first restoration pathset Case 4: Two failures isolating a degree-2 node  not possible by definition in a restorable network  no outage  may be outage  certain outage  may be outage Unavailability Sequences:

E E Module 18 © Wayne D. Grover 2002, Impact of Dual Failures Example #1, no spatial interaction:  NO OUTAGE W = 3 3 W = 2 2 The two restoration paths do not interfere W: working capacity S: spare capacity

E E Module 18 © Wayne D. Grover 2002, Impact of Dual Failures W: working capacity S: spare capacity W = 3 W = 2 Example #2, spatial interaction - capacity dependency: S < 5 or S > 5 ? Is there enough spare capacity to restore both failures?  POSSIBLE OUTAGE depending on the value of S

E E Module 18 © Wayne D. Grover 2002, Impact of Dual Failures W: working capacity S: spare capacity W = 2 2 The second failure hits the restoration path set deployed for first failed span The outcome in this situation depends on the adaptability of restoration mechanism and on the amount of remaining spare capacity  POSSIBLE OUTAGE Example #3, spatial interaction - special case:

E E Module 18 © Wayne D. Grover 2002, Impact of Dual Failures W = 3 Nothing can be done to restore any of the two failures  OUTAGE W = 2 W: working capacity S: spare capacity Example #4, isolated node:

E E Module 18 © Wayne D. Grover 2002, Adaptability of the restoration mechanism S2S2 S1S1 S2S2 S1S1 S2S2 S1S1 Static behaviour Partly adaptive behaviour Fully adaptive behaviour Restoration preplan says: “S 2 is to be restored through S 1 ” S 2 is restored via another route where spare capacity is available S 1 is left unrestored S 2 is restored via another route where spare capacity is available S 1 is restored again (if possible) with release of spare capacity previously used for restoration of span S 1 (similar to path restoration’s stub release) Optional: The spare capacity used on span S 2 gets “working status” and benefits from restoration effort for S 2

E E Module 18 © Wayne D. Grover 2002, Results of Case Studies * Designed with Optimal Modular Spare Capacity Placement Typical test network : R 2 Results for 5 test networks: With a fully adaptive behaviour in a modular environment the working units enjoy almost full restorability to any dual span failures

E E Module 18 © Wayne D. Grover 2002, Improvement over a non-restorable network Path availability improvement example: Test network: EuroNetA (19 nodes, 37 spans) Reference path: 5 hops Assumption: Us=3  If the network is non-restorable: U link = U span, U path = 15  = 13 hrs/year If the network is restorable, the simulation with fully adaptive behaviour gives: R 2 =  U link * = 9.18   U path = 4.59  = 2.4 min/year Making a network restorable to single span failures brings a considerable improvement in the average availability of service paths. For specific services it might still not be enough … How can we make service paths even more available?

E E Module 18 © Wayne D. Grover 2002, Design for High Availability The idea is to provision a network from an availability standpoint. Two integer programming formulations were developed: Dual Failure Minimum Capacity (DFMC) Finds the minimum capacity assignment for full restorability to dual-failures (R 2 =100%) note: Cannot be used for networks with any degree-2 graph cut. Dual Failure Max Restorability (DFMR) Finds the spare capacity placement that maximizes the average restorability to dual-failures for a given spare capacity budget.

E E Module 18 © Wayne D. Grover 2002, R 2 Design - Experimental Results Cost of improving the R 2 restorability: Spare capacity for R1=100%  223 units (55% redundancy) Total working: 405 Spare capacity for R2=100%  628 units (155% redundancy) To go from R2 = 80% to R2 = 100% we need to almost TRIPLE the spare capacity

E E Module 18 © Wayne D. Grover 2002, Conclusion of R2 studies It is very costly to guarantee R 2 restorability to all service paths in the network. However in most cases of dual span failures the restoration mechanism is able to restore part or all of the failed working units Idea: For little or no extra capacity it should be possible to guarantee full restorability to dual failures to selected network connections W = 3 (including 1 with higher priority) W = S = 3 Constraint modification for the Dual Failure Minimum Capacity formulation (DFMC): instead of “For any dual failure restoration paths must be found for all failed working units” we now have “For any dual failure restoration paths must always be found for all working units requiring R2 restorability” Restoration of higher priority connection

E E Module 18 © Wayne D. Grover 2002, Multi-Priority Mesh Design In case of single failure of span i, restore all working units that require R 1 restorability In case of failure of spans i and j, restore all working units (x i ) that require R 2 restorability Route p cannot be used if it crosses one of the two failed spans Spare capacity is needed to support restoration of single span failures Spare capacity is needed to support restoration of dual span failures Subject to:

E E Module 18 © Wayne D. Grover 2002, Network Availability Simulator The simulator determines times of span failures and repairs according to given statistical distributions: span 12 span 5span 2 span 5span 8 At each stage: Set of failed spans Restoration analyzer Set of lost Connections Connections Outage Recorder stage 1stage 2stage 3stage 4stage 5stage 6 t The objective of the simulator is to obtain information about the availability of end-to-end network connections by generating span failures at random times and analyzing the restoration depending on connection priorities Characteristics of a network connection: Origin node Destination node Size (STS-1, STS-3, STS-12,…) Restorability Requirement (R0, R1, R2) Routing between O and D

E E Module 18 © Wayne D. Grover 2002, Network Availability Simulator Advantages of the simulator: Confirm results obtained with theoretical availability expressions based on R2. Obtain information about the distribution of outage times (1000 outages of 0.1 sec has a different impact than 10 outages of 10 sec) Possibility to use different distributions of time-to-repair and time-between-failure for each span.

E E Module 18 © Wayne D. Grover 2002, Mesh/Ring Availability Comparison Single span failures Mesh: Full protection Rings: Full protection Dual span failures with no spatial interaction Mesh: Full protection Rings: Full protection Dual span failures with spatial interaction Mesh: Protection from 0% to 100% of the working units depending on available spare capacity and adaptability of the restoration mechanism Rings (2 span failures on same ring): Protection of about 2/3 of the traffic (demands that are not isolated by the 2 span failures For connections requiring R1 restorability, the Ring-based solution and the mesh solution provide similar levels of availability

E E Module 18 © Wayne D. Grover 2002, Mesh/Ring Availability Comparison Possibility of guaranteeing R2 restorability : origin exit The connection is lost whatever his priority level is. Mesh Networks: Yes! With adequate design and an adaptive restoration mechanism. Ring Networks: No, in certain cases restorability to dual failures cannot be guaranteed Example: Conclusion: The mesh architecture seems to be more appropriate than rings to serve demands with high availability requirements