DISTANCE MEASURES FOR QOS PERFORMANCE MANAGEMENT IN MIXED NETWORKS

Transcription

1 DISAE MEASURES FOR QOS PERFORMAE MANAEMEN IN MIXED NEWORKS Yacob Astatke Department of Electrical and omputer Engineering Morgan State University Dr. Richard Dean Faculty Advisor ABSRA he integrated Network Enhanced elemetry effort (ine) was launched to create a telemetry network that will enhance the traditional point-to-point telemetry link from test articles (As) to ground stations (S). wo of the critical needs identified by the entral est and Evaluation Investment Program (EIP) are, the need to be able to provide reliable coverage in potentially high capacity environments, even in Over-he-Horizon (OH) settings, and the need to make more efficient use of spectrum resources through dynamic sharing of said resources, based on instantaneous demand thereof. Research conducted at Morgan State University (MSU) has focused on providing solutions for both critical problems. he Mixed Network architecture developed by MSU has shown that a hybrid network can be used to provide coverage for As that are beyond the coverage area of the S. he mixed network uses clustering techniques to partition the aggregate network into clusters or sub-networks based on properties of each A, which currently include signal strengths, and location. he paper starts with a detailed analysis of two parameters that affect the performance of each sub-network: contention between the As in the mobile ad-hoc network, and queuing at the ateway As that serve as the link between the mobile ad-hoc and the ellular networks. ontention and queuing will be used to evaluate two performance (distance) measures for each sub-network: throughput and delay. We define a new distance measure known as power, which is equal to the ratio of throughput over delay, and is used as a measure of performance of the mixed network for Quality of Service (QOS). his paper describes the analytical foundation used to prove that the power performance measure is an excellent tool for optimizing the clustering of a mixed network to provide QOS. KEYWORDS Quality of Service, Mixed Network, Adhoc, ontention, hroughput,delay, Power performance measures 1

2 I. INRODUION he integrated Network Enhanced elemetry effort (ine) was launched to create a telemetry network that will enhance the traditional IRI-106 point-to-point telemetry link from test articles (As) to ground stations (S). Research conducted at Morgan State University (MSU) has focused on providing solutions for two important critical needs identified by the entral est and Evaluation Investment Program (EIP). hey are: the need to be able to provide reliable coverage in potentially high capacity environments, even in Over-he-Horizon (OH) settings, and the need to make more efficient use of spectrum resources through dynamic sharing of said resources, based on instantaneous demand thereof. he Mixed Network architecture developed by MSU has shown that a cellular-adhoc hybrid network can be used to provide coverage for As that are beyond the coverage area of the S, while maintaining the desired level of QOS for all the As in the network. he mixed network uses clustering techniques to partition the aggregate network into clusters or sub-networks based on properties of each A, which currently include signal strengths, location, throughput and delay [1][2]. he paper starts with an overview of the mixed network architecture followed by an analysis of two parameters that affect the performance of each sub-network: contention between the As in the mobile ad-hoc network, and queuing at the ateway As that serve as the link between the mobile ad-hoc and the ellular networks. ontention and queuing will be used to evaluate two performance measures for each sub-network: throughput and delay. Finally, a discussion of new distance measure known as power is presented. his paper ends with the discussion of the analytical and simulation results used to prove that the power performance measure is an excellent tool for optimizing the clustering of a mixed network to provide QOS for various applications. II. MIXED NEWORK ARHIEURE he Mixed Network designed by MSU is based on the following assumptions. It uses an optimized clustering scheme based on distance and angle developed by Babasola [1] to divide the network into a cellular with a S and one or more ad-hoc sub-networks also known as cluster cells (). It is assumed that the S is located at the center of the cellular network, and that several s are located around it as shown in Figure 1. All As are equipped with dual interface Network Interface ards (NI) that allows them to operate in cellular mode (M), ad-hoc mode (AHM) or gateway mode (M) depending on their location from the S. It is also assumed that each A is aware of its geographical location at all times through PS or any other positioning mechanism. ateway nodes (N) are capable of communicating in both cellular and ad hoc mode simultaneously and they can be used to relay data from As that are operating in OH settings to the S or vice versa. More information regarding the architecture of the mixed network can be found in [1]. In a typical ine scenario As that are operating in either M, AHM, or M send their data to the S for processing. he choice of the Ns is very important to the overall performance of the network because they can create a bottleneck between the adhoc network and the cellular network. 2

3 Ad-hoc Node luster cells ateway node round Station ellular Nodes ateway node Figure 1: Mixed Network Overview (source [1]) A closer look of the region in the mixed network where the Ns are located indicates that the performance of the mixed network depends on the interaction between the As in the two sub-networks. here are two important parameters that affect the performance of each sub-network: contention between the As in the mobile ad-hoc network, and queuing at the ateway As that serve as the link between the mobile ad-hoc and the ellular networks as shown in Figure 2. ontention and queuing will be used to evaluate two performance or distance measures for each sub-network: throughput and delay. he two distance measures will then be used to manage the overall performance of the mixed network for QOS applications. ateway A round Station ontention Ad-Hoc Network Queue ellular Network Figure 2: Effect of ontention and Queuing III. ONENION AND QUEUIN IN HE MIXED NEWORK A. ontention Model he most widely used wireless standard that defines all aspects of radio frequency based wireless networking is IEEE here are two kinds of wireless networks: infrastructure based and infrastructure-less. Infrastructure based wireless networks use an access point, or a base station to provide connectivity for the wireless nodes that are 3

4 within their range of coverage. Infrastructure-less wireless networks, which are also known as ad-hoc, or peer-to-peer networks consist of wireless nodes (As) that can communicate directly with all of the other wireless enabled nodes (As). urrent Mobile Ad-hoc Networks (MANE) face two critical issues: routing and multiple access, that affect their effective implementation. he lack of any centralized control and possible node mobility in ad-hoc wireless networks give rise to many issues at the network, medium access, and physical layers, which have no counterparts in the wired networks or in cellular networks. he Medium Access ontrol (MA) protocols have to be able to allow as many nodes as possible to simultaneously use the available bandwidth while avoiding interference and collisions. here are two types of MA protocols. he first one is the distributed, or contention based (random) MA protocol that is suitable for infrastructure-less ad-hoc networks. he second one is the centralized, or conflict-free (scheduled) MA protocol that is suitable for infrastructure based networks. he main difference between the two MA protocols is that packets transmitted using contention-based protocols dont have any guarantee to be successfully delivered. he first contention based MA protocol is the pure ALOHA protocol [3] where nodes transmit randomly without checking whether the channel is free or not. It was then followed by the Slotted-ALOHA MA protocol [4] where nodes transmit randomly but within a given time slot. Although this approach still suffers from collisions, the use of time slots minimizes the number of collisions compared to the pure ALOHA scheme. he slotted-aloha MA protocol was then replaced by the arrier Sense Multiple Access with ollision Detection (SMA/D) or ollision Avoidance (SMA/A) protocols [5] that allows nodes that to avoid collisions by listening to the channel before transmitting. he theoretical and experimental results discussed in this paper are based for reasons of simplicity on the Slotted-Aloha contention protocol shown in Figure 3. Figure 3: Slotted Aloha protocol Source: Wikipedia - ALOHA 4

5 B. Queuing model Queuing plays an important role in the overall performance of the mixed network because it affects the performance of the Ns. A queue is usually referred as a collection of packets temporarily stored in one or more buffers waiting to be processed. he most commonly used queue processing technique is the first in first out (FIFO) technique, where items are processed in the order that they are received. Both single server or multi-server queues can be used to provide service for two kinds of traffic: priority and non-priority. Queuing theory can be used to derive several queue related performance measures such as the average waiting time in the queue or the system, the expected number of items waiting or receiving service and the probability of encountering the system in different states such as empty, full, etc he theoretical and experimental results discussed in this paper are based on the single server queue model shown in Figure 4, and discussed in detail in [7]. Figure 4: Single Server Queue Example he characteristics of a queuing model can be described using the Kendals A/B/ queuing notation, where A represents the interarrival time distribution, B the service time distribution, and as number of servers. In our case, we used the M/M/1 queue, which is the most basic and important queuing model. It assumes Poisson arrivals with some rate, Exponential service times, a single server, and an infinite length buffer. IV. POWER DISAE MEASURE FOR HE MIXED NEWORK A. Analytical Derivations for ontention he analytical derivation of the throughput and delay distance measures for the contention constrained ad-hoc network is based on the assumption that the slotted Aloha multiple access contention protocol is used. If we assume that we have contention network with demand, the probability of no collision is given by [6]: P e, which implies that the probability of collision can be expressed as: P 1 P 1 e. he throughput of the contention network can be expressed as: S ( ) e (1) he delay of the contention network can be derived by aggregating the delay due to collision experienced by As that have to wait n attempts with time slots, and based on their back-off window size A before sending their data. he final delay equation can be derived by induction as follows: 5

6 Delay( ) P na P P P P na P (2) n 0 n n 0 n n 0 Each term of the delay expression can be rewritten as follows: n i 1 P P is of the form x n 0 i 0 1 x n i x P na P is of the form ix 2 n 0 i 0 1 x n P n P A P herefore: P P and P na P 2 n 0 1 P n 0 1 P P P A P AP AP Delay( ) (3) 2 1 P 1 P 1 P P n Using P 1 P. he overall delay is shown below: AP A 1 e Delay( ) (4) P e B. Analytical Derivations for Q he analytical derivation of the throughput and delay distance measures for the q constrained network where the Ns operate is based on the assumption that we have an M/M/1 queuing system. he relationship between the service time, the time in the system or total queue time q, and the utilization of the system is derived in [7]: q (5) 1 Lets assume for a system with QOS requirements that a packet will be dropped if its queue time q is greater than some maximum threshold wait time max. he probability that the packet will be not be dropped (i.e. q < max ) can be derived as follows [7]: 1 max s P q max 1 e (6) If we assume that the delay of the queuing network is equal to the wait time: Delay( ) w (we are assuming demand = ) (7) 1 1 he throughput of the queuing network can be derived as follows: throughput *[Pr ob _ Not _ Dropped] * P E q E throughput max 1 max s *[Pr ob _ Not _ Dropped] * 1 e (8). Analytical derivations for the Power Distance Measure Performance measures play a critical role in the design of any communication system because they can be used to evaluate and compare it to other systems. he throughput and delay performance measures were derived separately for the contention and queuing networks. However, we need to use a homogeneous performance measure such as 6

7 power in order to characterize the overall performance of the mixed network and synthesize the effect of the various competing performance metrics (such as throughput and delay). he power performance function can be used to identify a communication systems optimum (best) performance point. he simple power function for a system at some load is expressed as follows [7]: hroughput _ cont( ) Power ( ) (9) Delay _ cont( ) he analytical power distance measure for the contention sub-network can be derived using equations (1) and (4) as follows: hroughput _ cont( ) e Powercont ( ) (10) Delay _ cont( ) A 1 e e he analytical power distance measure for the queuing sub-network can be derived using equations (7) and (8) as follows: 1 max * 1 e hroughput _ del( ) Powerque ( ) (11) Delay _ del( ) 1 Since contention and queuing affect the overall performance of the mixed network, the aggregate throughput and delay due to both parameters can be computed as follows: Aggregate _ hroughput hroughput cont * hroughput que Aggregate 1 max _ hroughput e * * 1 e s (12) Aggregate _ Delay Delay cont Delay que A 1 e Aggregate _ Delay (13) e 1 he aggregate power distance measure based on both contention and queuing is : Aggregate _ Power( ) Aggregate _ hroughput Aggregate _ Delay Aggregate _ Power( ) e * * 1 A 1 e e e 1 max 1 (14) 7

8 D. Simulation results and discussions he analytical results derived in the previous three sections were simulated using the MALAB simulation software. It should be noted that the value of 20 on the x axis in all the plots represents a demand of 100%. he aggregate delay and throughput of the mixed network are shown in figures 6 (a) and (b). Our results are similar to other theoretical results [6],[7], and prove that our analytical derivations are correct. he aggregate throughput plots indicate a maximum value of around 15%. his is expected because the maximum throughput of the slotted aloha contention protocol is around 37%. herefore, when we combine the effect of contention and q, we expect the overall performance to deteriorate even more. he aggregate power plot for the simple power function shown in figure 6(c) is similar to the theoretical results presented in [7], and proves that our analytical derivations are once again correct. he simple power function defined in equation (9) allows the user to identify only one particular operating point for the network. herefore, we modified the simple power function in order to adapt it to the QOS requirements of our mixed network. Our goal is to use the power function to optimize the clustering of a mixed network to provide QOS for various applications based on their throughput or delay requirements. We modified the simple power function by adding weights as exponents to each distance measure as shown in equation (15). w _ thr ( Aggregate _ hroughput) Modified _ Aggregate _ Power( ) (15) w _ del ( Aggregate _ Delay) We added the weights as exponents of each performance measure, because they allow us to vary the operating point of the power performance measure along the x axis based on the throughput or delay requirements. he two weighting factors (w_thr, and w_del) can be used to put an emphasis on either the throughput or delay. his implies that we can optimize the clustering of a mixed network to provide QOS for various applications based on their throughput or delay requirements. he plots in figure 6(d) indicate that the mixed network can be optimized for a performance that puts an emphasis on either throughput or delay. his implies that the clustering scheme for the mixed network proposed by [1] can now be modified to include two additional performance measures such as throughput and delay as shown in equation (16). w_dist w_ang w _ thru w _ del luster [ Dist,Angle,hru,Delay ] (16) 8

9 Figure 6 Performance plots versus Demand V. OLUSIONS AND FUURE WORK We have shown that both contention and queuing affect the overall performance of the mixed network because of their impact on two distance measures: throughput and delay. We have derived analytical results for the throughput and delay performance measures for our mixed network. We chose the slotted aloha contention protocol because of its simplicity in the derivations of a closed form solution for the delay and throughput. We plan to expand our analysis to include the most commonly used SMA/A contention protocol. We have also derived analytical results for a modified power performance measure and have shown through simulations that it can be used to optimize the performance of the mixed network based on QOS requirements. he next step in our research is to derive the appropriate weights for the power function and apply them to expand the clustering scheme shown in equation (16). his will allow us to re-cluster and optimize the performance of the mixed network for QOS applications based on distance, angle, throughput, delay, and any other performance measures such as interference and frequency management. We have demonstrated that the power distance measure is a powerful tool that can be used to optimize the overall performance of a communication system. 9

10 AKNOWLEDMENS I would like to thank my advisor Dr. Richard Dean, director of the Signals ommunications and Networks (SN) Lab at MSU, and Mr. Antwan haney (M.Eng 08) for doing most of the simulation work. REFEREES [1] Babalola, O. A., Optimal onfiguration For Nodes In Mixed ellular And Ad Hoc Network For Inet, I 2007 onference, Las Vegas, NV, October [2] St. Ange, L., he Optimization of Node onfiguration in a Mixed Network for Quality of Service, I 2008 onference, San Diego, A, October [3]A. handra, V. ummalla, and J. O. Limb, Wireless Medium Access ontrol Protocols. IEEE om. Surveys & utorials, Second Quarter Available from [4] N. Abramson, he hroughput of Packet Broadcasting hannels, IEEE ransactions on ommunications, 25(1):117128, [5] L. Kleinrock and F.A. obagi, Packet Switching in Radio hannels: Part I - arrier Sense Multiple-access modes and their throughput-delay characteristic, IEEE ransactions on ommunications, OM-23, [6] Agrawal, P.D, Zeng, Q.A., Introduction to Wireless and Mobile Systems, homson Learning, [7] Stallings, W., High-Speed Networks and Internets Performance and Quality of Service, Second Edition, Prentice Hall,