The BFMLM-FQ

CHAPTER VII
BFMLM-FQ ALGORITHM

7.1 INTRODUCTION

The concept of Logical Layer Survivability Parameters Module using TWO-TIER algorithm was described in the previous chapter. The enhanced multilayered connectivity survivability and global fair queuing techniques by using the Bounded-Fair Maximize-Local-Min Fair Queuing (BFMLM-FQ) packet scheduling algorithm and the simulation results are discussed in this chapter.

Lisong XU Group [26] depicted the fair channel sharing and limited channel reuse with the generation of Enhanced Maximize -Local-Min Fair Queuing (EMLM-FQ) algorithm as shown in Fig. 7.1. Their model described the path propagation in which the short range of packet propagation was only estimated and it cannot achieve the global fairness model.

In order to overcome this limitation, a new BFMLM-FQ is introduced in this chapter. The BFMLM-FQ provides the spatial channel reuse by using the concept of node mobility and node scalability in optical adhoc networks. It can achieve the global range of optical packet scheduling design issues by using fairness ad-hoc network architecture analysis [54].

7.2 FAIRNESS AD-HOC NETWORK ARCHITECTURE ANALYSIS

Fair queuing is already a popular paradigm in optical networks. The parameters like scalability, mobility and efficient fair queuing are largely unaddressed in ad-hoc network environment. The previous work proposed the resource management algorithms and protocols were designed for communication networks [22]. It determines the simple analysis in which low capacity connectivity and minimal throughput per flow was achieved.

The present work describes the multihop flow propagation which is divided into a number of single hops to achieve the global topology independent fairness model. It seeks to improve the design of the distributed implementation to perform more extensive simulations by using BFMLM-FQ fair packet scheduling algorithm [55].

7.3 The BFMLM-FQ SCHEDULING ALGORITHM ASSUMPTIONS

The proposed BFMLM-FQ Optical ad-hoc fair packet scheduling algorithm is fully decentralized and localized in order to achieve node mobility and scalability.

7.3.1 GLOBAL FAIRNESS MODEL - BFMLM-FQ

The global fairness model can be achieved by using the following factors.

STEP 1: Fair Queuing Flow Achievement

BFMLM-FQ algorithm achieves the fair queuing flow assignment. In this each queue co-ordinates with its neighbour and depicts the flow information across each node graph and flow graph respectively. A node cannot transmit and receive packets simultaneously, and flow contention is commutative.

STEP 2: BFMLM-FQ Flow Information Propagation

A distributed achievement of the local fairness model within the optical ad-hoc connectivity by BFMLM-FQ must be used, where the flow information is to be propagated among the multihop connectivity flow.

7.4 MULTILAYERED SELF-COORDINATING LOCALIZED GLOBAL

FAIR QUEUING IN OPTICAL AD-HOC NETWORKS

Fair queuing in ad-hoc networks results in increasing the channel capacity in terms of spatial channel reuse. In this a simultaneous transmission of flow propagation is achieved and collisions are avoided.

To meet these requirements, Bounded Fair Maximize-Local-Minimum Fair Queuing (BFMLM-FQ), is proposed in which local schedulers self-coordinate their scheduling decisions and collectively achieve fair bandwidth sharing [53]. It also achieves statistical short-term throughput and delay bounds over the shared optical channel. Analysis and extensive simulations confirm the effectiveness of the proposed self-coordinating design which achieves global fair channel access in optical ad-hoc networks [52]. In this fair distribution of bandwidth and maximization of resource utilization is achieved [53].

7.5 NUMERICAL RESULTS

The numerical results for BFMLM-FQ Scheduling Algorithm for 11 X 11 node connectivity is computed and presented in terms of input parameters like network connectivity, node connectivity and flow connectivity are as shown in tables 7.2 and the output parameters like throughput connectivity and spatial channel reuse are as shown in table 7.3. The node graph and corresponding flow graphs are illustrated.. By using network simulator (ns2) model the global fairness model of BFMLM-FQ algorithm was achieved in terms of throughput and packet distribution in ad-hoc network connectivity as shown in Fig. 7.6.

In the previous work Lisong XU Group et.al. Group represented the EMLM-FQ Algorithm, in terms of path propagation of short range packets. This model suffered from establishing multipath connectivity and also in achieving the global fairness.

The overall throughput is 92% as compared to 15% of the previous work with explicit coordination to reduce collisions of packet scheduling in an optical-ad-hoc network environment.

7.6 DISCUSSIONS

The previous work by Lisong XU Group describes the performance of limited channel capacity in fair queuing model. The present work BFMLM-FQ determines the global fairness model by using spatial channel mobility and scalability techniques in optical ad-hoc networks packet scheduling procedures. It describes the co-ordination between local and global parameters like node connectivity, location dependent and packet delivery system to achieve the global fairness model.

Network Architecture Logical Layer Survivability Parameters Module by using BFMLM-FQ model has been analyzed in this chapter. It can be extended further to evaluate the fair-queuing integration techniques by using Hybrid fair packet scheduling algorithm described in the next chapter.

S: the set of nodes in the graph

V: a node in set G

N(v): adjacent node set of v

d(v): degree of node v

B: output set

B ß Ф

while S ≠ Ф

(1) Set D to NULL. For each flow,

if the start tag of the head of line packet of a flow is not greater than

V (t) + LP,

then set the state of the flow to contend, else set the state of the flow to no-contend.

(2) If there is no flow in contend state, then add the flow with the minimum start tag to D and skip to the next step. Otherwise, while there are flows in the contend state, select the flow f with the minimum finish tag of the head of line packet and add f to the set D.

Set all flows in the closed neighborhood of D, N [f], to no-contend.

(3) Update the virtual time V (t) to the maximum start tag of the head of line packets among flows in D. Update the start and finish tags of the flows in D.

(4) Select the maximum independent set S in the graph G - N [D].

(5) Schedule the flows in S D for transmission. Do not increment the start and finish tags of the flows in S.

Table 7.1 BFMLM-FQ Parameters Comparison Table

Algorithms

Issues

BFMLM-FQ

Location-dependent

Contention

True

Spatial channel reuse

True

Conflicts between

ensuring fairness and

maximizing channel

utilization

False

Distributed nature

True

Node mobility

True

Scalability

True

Table 7.2 BFMLM-FQ Packet Scheduling Algorithm For Optical

Ad-Hoc Networks Input Table

INPUT TABLE NETWORK CONNECTIVITY

NODE CONNECIVIY

FLOW CONNECIVITY

1

1-2

1-2,1-3,1-4,1-5,1-6, 1-7,1-8,1-9

2

2-5

2-1,2-3,2-4,2-5,2-6, 2-7,2-8,2-9

3

----

3-1,3-2,3-4,3-5,3-6, 3-7,3-8,3-9

4

4-3

4-1,4-2,4-3,4-5,4-6, 4-7,4-8,4-9

5

----

5-1,5-2,5-3,5-4,5-6, 5-7,5-8,5-9

6

6-5

6-1,6-2,6-3,6-4,6-5, 6-7,6-8,6-9

7

7-10

7-1,7-2,7-3,7-4,7-5, 7-6,7-8,7-9

8

8-11

8-1,8-2,8-3,8-4,8-5, 8-6,8-7,8-9

9

9-8

9-1,9-2,9-3,9-4,9-5, 9-6,9-7,9-8

10

10-9

10-1,10-2,10-3, 10-4,10-5,10-6, 10-7,10-8,10-9

11

11-10

11-1,11-2,11-3, 11-4,11-5,11-6,11-7 , 11-8, 11-9,11-10

Table 7.3 BFMLM-FQ Packet Scheduling Algorithm For Optical

Ad-Hoc Networks Output Table

NODES

LOCATION DEPENDENT

THROUGHPUT (%)

166

SPATIAL CHANNEL REUSE (%)

66

FAIRNESS & MAXIMIZING CHANNEL

NO

NODE MOBILITY

YES

SCALABILITY

YES

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