A New Token Allocation Algorithm for TCP Traffic in Diffserv Network

Similar documents
Simulation Based Analysis of FAST TCP using OMNET++

Intelligent Traffic Conditioners for Assured Forwarding Based Differentiated Services Networks 1

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

Avoiding congestion through dynamic load control

Efficient Distributed File System (EDFS)

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION

Internet Traffic Managers

Multi-objective Design Optimization of MCM Placement

NGPM -- A NSGA-II Program in Matlab

Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments

State of the Art in Differentiated

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

Determining the Optimal Bandwidth Based on Multi-criterion Fusion

Support Vector Machines

Network Coding as a Dynamical System

Parallelism for Nested Loops with Non-uniform and Flow Dependences

CS 268: Lecture 8 Router Support for Congestion Control

Smoothing Spline ANOVA for variable screening

High Performance DiffServ Mechanism for Routers and Switches: Packet Arrival Rate Based Queue Management for Class Based Scheduling

Load-Balanced Anycast Routing

Routing in Degree-constrained FSO Mesh Networks

Gateway Algorithm for Fair Bandwidth Sharing

Advanced Computer Networks

RAP. Speed/RAP/CODA. Real-time Systems. Modeling the sensor networks. Real-time Systems. Modeling the sensor networks. Real-time systems:

An Optimal Algorithm for Prufer Codes *

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Efficient Load-Balanced IP Routing Scheme Based on Shortest Paths in Hose Model. Eiji Oki May 28, 2009 The University of Electro-Communications

PERFORMANCE ANALYSIS OF AF IN CONSIDERING LINK

Neural Network Control for TCP Network Congestion

Integrated Congestion-Control Mechanism in Optical Burst Switching Networks

Pricing Network Resources for Adaptive Applications in a Differentiated Services Network

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

GA-Based Learning Algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks

Solutions for Real-Time Communication over Best-Effort Networks

Load Balancing for Hex-Cell Interconnection Network

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

CS 534: Computer Vision Model Fitting

A fair buffer allocation scheme

F Geometric Mean Graphs

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

A Genetic Algorithm Based Dynamic Load Balancing Scheme for Heterogeneous Distributed Systems

Analysis of Collaborative Distributed Admission Control in x Networks

HIERARCHICAL SCHEDULING WITH ADAPTIVE WEIGHTS FOR W-ATM *

Wishing you all a Total Quality New Year!

S1 Note. Basis functions.

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

DESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT

Priority-Based Scheduling Algorithm for Downlink Traffics in IEEE Networks

Meta-heuristics for Multidimensional Knapsack Problems

Sample Solution. Advanced Computer Networks P 1 P 2 P 3 P 4 P 5. Module: IN2097 Date: Examiner: Prof. Dr.-Ing. Georg Carle Exam: Final exam

SciFed Journal of Telecommunication Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network

EVALUATION OF THE PERFORMANCES OF ARTIFICIAL BEE COLONY AND INVASIVE WEED OPTIMIZATION ALGORITHMS ON THE MODIFIED BENCHMARK FUNCTIONS

Simulation and Exploration of RCP in the networks

Quantifying Responsiveness of TCP Aggregates by Using Direct Sequence Spread Spectrum CDMA and Its Application in Congestion Control

Reliable and Efficient Routing Using Adaptive Genetic Algorithm in Packet Switched Networks

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Control strategies for network efficiency and resilience with route choice

Optimization of Adaptive Transit Signal Priority Using Parallel Genetic Algorithm

X- Chart Using ANOM Approach

Backpropagation: In Search of Performance Parameters

Fuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches

A GENETIC ALGORITHM FOR PROCESS SCHEDULING IN DISTRIBUTED OPERATING SYSTEMS CONSIDERING LOAD BALANCING

Programming in Fortran 90 : 2017/2018

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints

A Novel Approach for an Early Test Case Generation using Genetic Algorithm and Dominance Concept based on Use cases

Genetic Algorithm-based Hybrid Methods for a Flexible Single-operation Serial-batch Scheduling Problem with Mold Constraints

Some Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.

GSLM Operations Research II Fall 13/14

Multiobjective fuzzy optimization method

Research on Kruskal Crossover Genetic Algorithm for Multi- Objective Logistics Distribution Path Optimization

An Optimal Bandwidth Allocation and Data Droppage Scheme for Differentiated Services in a Wireless Network

QoS-aware composite scheduling using fuzzy proactive and reactive controllers

MobileGrid: Capacity-aware Topology Control in Mobile Ad Hoc Networks

Optimization of Local Routing for Connected Nodes with Single Output Ports - Part I: Theory

Virtual Machine Migration based on Trust Measurement of Computer Node

Positive Semi-definite Programming Localization in Wireless Sensor Networks

Bandwidth Allocation for Service Level Agreement Aware Ethernet Passive Optical Networks

Resource and Virtual Function Status Monitoring in Network Function Virtualization Environment

Application of Improved Fish Swarm Algorithm in Cloud Computing Resource Scheduling

Delay Variation Optimized Traffic Allocation Based on Network Calculus for Multi-path Routing in Wireless Mesh Networks

Design for Reliability: Case Studies in Manufacturing Process Synthesis

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning

Comparisons of Packet Scheduling Algorithms for Fair Service among Connections on the Internet

ARTICLE IN PRESS. Signal Processing: Image Communication

Why Congestion Control. Congestion Control and Active Queue Management. TCP Congestion Control Behavior. Generic TCP CC Behavior: Additive Increase

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay Metrics

Goals and Approach Type of Resources Allocation Models Shared Non-shared Not in this Lecture In this Lecture

OPTIMIZATION OF FUZZY RULE BASES USING CONTINUOUS ANT COLONY SYSTEM

Effect of Number of Drop Precedences in Assured Forwarding

Biostatistics 615/815

C2 Training: June 8 9, Combining effect sizes across studies. Create a set of independent effect sizes. Introduction to meta-analysis

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Unsupervised Learning

Application of VCG in Replica Placement Strategy of Cloud Storage

PERFORMANCE ANALYSIS OF AF IN CONSIDERING LINK UTILISATION BY SIMULATION WITH DROP-TAIL

Elsevier Editorial System(tm) for Expert Systems With Applications Manuscript Draft

PARETO BAYESIAN OPTIMIZATION ALGORITHM FOR THE MULTIOBJECTIVE 0/1 KNAPSACK PROBLEM

A Fair Access Mechanism Based on TXOP in IEEE e Wireless Networks

Future Generation Computer Systems

Transcription:

A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network S. Sudha and N. Ammasagounden Natonal Insttute of Technology, Truchrappall, Taml Nadu 620 015 sudha@ntt.edu and ammas@ntt.edu Abstract A new algorthm has been desgned for provdng optmal allocaton of tokens for TCP traffc n DffServ networks, whch s amed at mnmzng the packet losses of the ndvdual flows. The optmalty n token allocaton s acheved usng optmzaton technque employng Genetc Algorthm (GA). The transent performance of the proposed algorthm has been evaluated through Matlab smulaton. It has been observed from the smulaton studes that the token allocatons vary wth respect to the ncomng rate of the flows. A TCP frendly three color marker has also been proposed to mnmze the spacng between the subsequent IN and OUT packets. The formulaton of the objectve functon and the applcaton of GA for the optmzaton process have been explaned. The smulaton results pertanng to the token allocaton for two dfferent cases are furnshed. Keywords: Optmzaton, Genetc Algorthm, Token Allocaton, Packet Markng, DffServ networks. I. Introducton The Dfferentated Servces (DffServ) archtecture has been proposed as a scalable soluton for provdng servce dfferentaton among flows wthout any per-flow buffer management nsde the core of the network [1]. The buldng blocks of the assured servce nclude a traffc marker that marks packets as IN and OUT dependng upon the servce level agreement(sla) at the edge of the doman and an actve queue management technque such as RIO (RED wth IN / OUT ) n the network nteror [2],[3]. Prevous works on markers provde good per-flow markng but do not consder far token dstrbuton between ndvdual flows of the aggregate ncomng traffc [4],[5]. The servce goals promsed by the DffServ network are not lkely to be acheved n many stuatons due to the TCP congeston control algorthm, whch was developed for the best effort servce and has no knowledge about the prortes of packets [6] - [8]. Ibanez et al. have used a token bucket marker for assured servce and showed that t resulted n TCP not realzng the mnmum assured servce [9]. Feroz et al. have proposed a TCP frendly marker to mnmze the spacng between the subsequent IN packets to overcome the bursty nature of the traffc, whch s the man cause for the packet losses n TCP. However, there s no farness n allocaton of tokens to short-lved TCP flows [10]. Renjsh Kumar et al. have proposed two markers namely the Memory-based two Internatonal Journal of The Computer, the Internet and Management Vol. 16.No.3 (September-December, 2008) pp 41-47 41

S. Sudha and N. Ammasagounden color marker and Memory- based three color marker for TCP frendly traffc based on the rate estmator [11]. The bursty nature s resolved by means of probablstc markng. However, ths marker s TSW-TC based. II. Proposed Work In the present paper, a novel technque s proposed to dstrbute the tokens optmally among the TCP flows employng TB-TC usng genetc algorthm. A three colour packet markng algorthm s also presented. The proposed algorthm ams at optmal allocaton of tokens to small wndow flows as well as for others based on the avalablty of the tokens. Hence a mult-objectve functon s requred to be formulated for the optmzaton process. The man feature of the proposed marker s to optmally allocate the avalable tokens such that flows of hgher sendng rate get more tokens than those of lower sendng rate to mnmze the packet losses of the ndvdual flows. Ths requrement s met by usng a weghtng factor n formulatng the objectve functon whch s evaluated by an optmzaton process usng GA. The output of the optmzaton process results n a token allocaton vector X and the default spacng vector O of sze f, where f corresponds to the fracton of the total flows. The objectve functon for the proposed work s formulated as: Mnmze f F(x) = w =1 ( p x ) (1) subject to the followng constrants : ) The total tokens allocated to f flows should not exceed the avalable tokens. Ths requrement s met by the constrant g1 as follows: g1 = =1 x Tk ) The tokens allocated to any ndvdual flow, should not exceed ther estmated sendng rate n order to avod wastage of tokens and at the same tme not less than zero( negatve value s not possble). Ths constrant g2 s enforced by the upper and the lower lmts of the desgn parameter as g 2 where p = estmate of the number of packets correspondng to flow x = number of tokens allocated to flow n = total number of flows f = number of small wndow flows or the remanng (.e. t s a fracton of n) Tk = number of tokens expressed as packets w = weght factor for flow, whch s expressed as the fracton of the estmated packets of flow to the total estmated packets of f flows.e.. p w = (2) Token Allocaton Algorthm f = 0 x p f = 1 Step I: Identfyng the flows and fndng the total of the estmated packets For each flow, f ( E < k) then /*Increment Small wndows and fnd the total of them */ Sw ++; fnd Σ p ; correspondng to Sw /*Increment remanng flows and fnd the total of them */ else R ++; fnd Σ p ; correspondng to R p 42

A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network Step II : Dstrbuton of the tokens among the flows ) If ( Tk < Σ P ; of Sw ) /* If no tokens left after protectng Sw flows */ For small wndow flows Dvde Tk among the Sw flows optmally Sw F( X ) = w P x =1 /* The vector X s obtaned for Sw flows usng GA */ subject to the constrants O = [ p / x ] -1 ; For all other flows x = 0 ( ) ) Else f (Tk Σ p ; of Sw ) /* To protect small wndow flows all the tokens are assgned to SW flows */ For small wndow flows All packets are assgned tokens x = p O = 0 For all other flows /* If tokens are left after protectng Sw flows */ Dvde the remanng tokens among the other flows optmally R F(x) = w =1 ( p x ) /* The vector X s obtaned for other flows usng GA */ subject to the constrants O = [ p / x ] -1 /* The spacng vector O s obtaned for other flow */ It s to be noted that the allocaton of tokens s done once n every T seconds. Thus by the above algorthm, the token allocaton vector X and the packet nterleavng vector O (.e. default spacng) for all the n flows are obtaned. The packets are marked based on the vectors X and O. In the markng nterval of T secs., the spacng s updated dynamcally dependng on the ncomng rate. The markng algorthm proposed s amed at provdng three dfferent levels of markng, namely green, yellow and red as explaned n the packet markng algorthm furnshed below. Packet markng Algorthm count = count of number of packets of flow ) Save the spacng varable for each flow n a temporary varable. Ds = O ) For every packet that comes n, dentfy ts flow. ) For flow, check f IN tokens are avalable.e. f (x > 0 ) v) If true then mark as follows : f ( count O ) /*packet count s larger than the spacng*/ mark as green O = ( Ds (count O )) Reset count decrement x else f ((count < O ) and ( count Ds )) mark as yellow Reset count else Mark as red v). Else Mark as red III. Optmzaton usng GA The lower and the upper lmts of the allocaton vector X correspondng to the ndvdual flows are ntalzed to 0 and p respectvely. Startng from the specfcaton of the populaton sze, number of generatons, cross-over and mutaton probablty, the optmzaton process s ntated. In general, GA maxmzes the gven functon whereas n the present case, Internatonal Journal of The Computer, the Internet and Management Vol. 16.No.3 (September-December, 2008) pp 41-47 43

S. Sudha and N. Ammasagounden the am s to mnmze the objectve functon (eqn.1). So, the functon s wrtten as ftval = 1/ (1+objval), where objval s the objectve functon value and ftval s the ftness functon value. The detaled steps for obtanng the convergence of the objectve functon are shown n flow chart of Fg. 1. As a result of ths optmzaton, the token allocaton and the default spacng for all the f flows are obtaned n a vector X and O respectvely. Start Get f, p and Tk as nput Intalaze POP, GEN, cross-over and mutaton probablty and the lower and upper bounds of the desgn varables. GEN=1 Generate the ntal populaton POP=1 Calculate the weghts usng eqn.(2), evaluate the objectve functon usng eqn.(1) and the ftness value POP=POP+1 No Is POP >pp? Yes Generaton of off-sprngs and a new set of chromosomes Choose the max ftness and mn Objval n the present GEN GEN=GEN+1 No Is GEN >gen? Yes Fg. 1 Flowchart for the optmzaton process. GEN : generaton count POP : populaton count Prnt mn objval and the token allocaton vector among all the Generatons 44

A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network IV. Smulaton Results The followng values have been chosen for smulaton n the optmzaton process: Number of generatons, gen = 50; populaton sze, pp = 30; mutaton probablty = 0.002; cross-over probablty = 0.96 The smulaton was carred out for 10 flows of whch the frst fve were assumed as small wndow flows. The estmaton of the number of packets correspondng to each flow was generated randomly. The random generator follows a unform dstrbuton n the range [0, 1]. To have the effect of a real network, the estmates were scaled up to hgher values. The smulatons have been carred out for two cases namely ) when the network load s hgh and ) when the network load s low. In the frst case, the small wndow flows are protected by optmally allocatng the avalable tokens only to them. In the second case, after protectng small wndow flows, the remanng tokens are optmally allocated to the other flows. The number of tokens was sutably ntalzed n both the cases. The smulaton scenaro consders the network topology shown n Fg. 2 where there s a bottleneck lnk. The results of the smulaton are plotted n the form of graphs shown n Fg. 3. The token allocaton of the ndvdual flows by the proposed algorthm s compared wth that of the TCP frendly marker suggested by [10]. From the plots, t s observed that the token allocaton vares wth the estmaton of the number of packets, thus provdng an optmal allocaton of tokens. The suggested packet markng algorthm mnmzes the spacng between the two subsequent IN (green) as well as OUT packets. The OUT packets are further categorzed as yellow and red whch wll be treated dfferently n the network nteror. The colors green, yellow and red represent drop precedences 0, 1, 2 respectvely of a sngle AF class. S0 D0 S9 S0 R1 : Bottleneck Marker : : S9 R3 R2 D20 Marker R4. Sources data Acks Destnatons Fg. 2 Network topology Internatonal Journal of The Computer, the Internet and Management Vol. 16.No.3 (September-December, 2008) pp 41-47 45

S. Sudha and N. Ammasagounden Token allocaton n packets 200 180 160 140 120 100 80 60 40 GA Marker TCP frendly Marker estmaton of the packets 20 0 1 2 3 4 5 6 7 8 9 10 Number of flows (a) Token allocaton n packets 150 125 100 75 50 25 GA Marker TCP-frendly marker estmaton of the packets 0 1 2 3 4 5 6 7 8 9 10 Number of flows (b) Fg. 3 Varaton of token allocaton wth flows ( n =10, Sw = 5, R =5) (a) when network load s hgh (b) when network load s low 46

A New Token Allocaton Algorthm for TCP Traffc n Dffserv Network V. Conclusons and Future Work In ths paper, a new algorthm has been proposed for token allocaton n a Dffserv network. The token allocaton algorthm suggested provdes an optmal dstrbuton of tokens to the flows, when the network load s hgh or low. A judcous formulaton of objectve functon combned wth GA technque for optmzaton has resulted n an optmal token allocaton vector for both small wndow flows and other flows. Further, a packet markng algorthm has also been proposed whch marks the packets as green, yellow or red n order to mnmze the spacng between the subsequent IN and OUT packets of these flows. It s expected that the packet losses may reduce by ths algorthm and hence better performance can be acheved n terms of tmeouts, goodput etc. However, ths has to be substantated by smulatng t n NS-2. As a further work, t s amed to evaluate the proposed TCP frendly marker through smulatons on NS-2 (Network Smulator). VI. References [1] S. Blake, D. Black, M. Carlson (1998), E. Daves, Z. Wang, and W. Wess, An archtecture for Dfferentated Servces, RFC 2475, December. [2] S. Floyd and V. Jacobson, (1993) Random Early Detecton gateways for congeston avodance, ACM/IEEE Transactons on Networkng, Vol.1, No. 4, pp 397-413. [3] D. D. Clark and W. Fang (1998), Explct allocaton of best effort packet delvery servce, IEEE/ACM Transactons on Networkng, Vol.6, No.4, pp. 362-373, 1998. [4] J. Henanen and R. Guern (1999), A two rate three color marker, RFC 2698, September. [5] J. Henanen and R. Guern (1999), A sngle rate three color marker, RFC 2697, September. [6] M. Banes, B. Nandy, P. Peda, N. Seddgh and M. Devestskots (2000), Usng TCP models to understand bandwdth assurance n a dfferentated servces network, Nortel Techncal Report, July. [7] I. Yeom, A. L. Narasmha Reddy (1994), Realzng throughput guarantees n a dfferentated servces network, Proc. ICMCS, Florence, Italy, 7-11, Vol.2, pp. 372-376, June. [8] V. Jacobson and M. J. Karels (1998), Congeston avodance and control, Proc. SIGCOMM 88, Stanford, CA, USA, pp. 314-329, August. [9] J. Ibanez and K. Nchols (1998), Prelmnary Smulaton Evaluaton of an assured servce, IETF Draft, August. [10] A. Feroz, A. Rao and S. Kalyanaraman (2000), A TCP - frendly traffc Marker for IP Dfferentated Servces, Proc. of the IEEE /IFIP 8 th Internatonal Workshop on Qualty of Servces, IWUQ o S. [11] K. R. Renjsh Kumar, A. L. Ananda, Llly Kutty Jacob (2002), TCP-frendly traffc condtonng n DffServ networks: a memory-based approach, Computer Networks, Vol 38, pp. 731-743. Internatonal Journal of The Computer, the Internet and Management Vol. 16.No.3 (September-December, 2008) pp 41-47 47