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Home | Sessions | Authors | Session 6.2 Buffer Allocation in Wireless Multimedia Networks Using Fuzzy Logic J. D. Mallapur, S. S. Manvi, R. B. Manjula D. H. Rao Basaveshwar Engineering College Bagalkot, Karnataka, INDIA Gogte Institute of Technology Belgaum, Karnataka, INDIA [email protected], [email protected] Abstract In future generation cellular systems, network resources like bandwidth, buffers, computing power, etc., have to be allocated with an efficient resource allocation method because of their scarcity. This paper presents a fuzzy based buffer allocation scheme for wireless multimedia networks in the context of future generation cellular networks. In this scheme, a buffer allocator located at the base station uses a fuzzy controller for buffer allocation that uses three fuzzy parameters namely application priority (based on handed and new calls), transmission rate, and packet size. Fuzzy controller computes an allocation factor, which is used to allocate the buffers to an requesting application. The scheme has been simulated in different network scenarios to test the operation effectiveness. The simulation results show that fuzzy based buffer allocation scheme performs better than conventional scheme in terms of buffer utilization, handoff and new calls acceptance. 1. Introduction Wireless multimedia networks are the need of today’s society. There is tremendous demand for multimedia applications over wireless cellular networks. Some of the applications are, video conferencing, Internet multimedia games, and e-commerce. To provide all such applications in wireless networks, we need to look at some of the pressing issues which arise due to scarcity of resources such as bandwidth, buffers, battery life, etc. Wireless and mobile links supporting packet data transfer require buffers to accommodate inherent load variations of various traffic and insufficient bandwidth allocated to a user. Issues in buffer allocation can be stated as follows. On one side, such a buffer needs to be sufficiently large to ensure good link utilization and low blocking probability, on the other side it needs to be small to minimize queuing delays. Hence there should be some buffer allocation technique which can take care of reducing delays as well utilize the links efficiently and accept more number of users in a cell. This paper presents a fuzzy based buffer allocation scheme by considering the fuzzy parameters such as priority of the application, rate [email protected] of transmission and packet size. Some of the works on buffer allocation for wireless multimedia networks are as follows. In [4], adaptive filter based dynamic buffer allocation method is presented that provides better Quality of Service (QoS) to an requesting application. A technique to reduce the waiting time portion of query processing through flexible buffer allocation is presented in [3]. An evaluation of a active queue management scheme tailored to specific characteristics of 3G links is carried out in [6]. A new queue management algorithm in order to utilize high data rate efficiently in current wireless LAN is given in [5]. In [7], resource allocation is done by using fuzzy logic, where network preferentially allocates its resources to real-time (RT) traffic sources and optimally allocates the remaining resources to NRT traffic based on channel fading conditions. An active queue management to limit the transmission queue length that eliminates expiration packet drops is discussed in [1]. A random early expiration detection based on buffer management algorithm for real-time traffic over wireless networks is presented in [2]. The rest of the paper is organized as follows. Section 2 describes the proposed fuzzy based buffer allocation scheme. Section 3 presents simulation model used to analyze the fuzzy based buffer allocation scheme. Results are presented in Section 4. Finally, Section 5 concludes the paper with some remarks. 2. Proposed Work The potential of mobile multimedia services is enormous. Another major challenge is the characterization of the resource (bandwidth, buffer, consumption power etc) requirements of multimedia documents. The multimedia communication application requires a class of network resources like high bandwidth, buffer and computing power. If the bandwidth required by each application is not available, then buffer plays a vital role. Hence for efficient utilization of the buffers placed at the base station, we propose a fuzzy based buffer allocation. In this section, we present the network environment, fuzzy based buffer allocation scheme and the algorithm for buffer allocation. 2.1. Network environment We consider a cell with several users operating in it. Users may come from other cells adjacent to a cell thus creating handoff calls. New calls can be generated within a cell with certain application requirements. Buffer allocation scheme exists at the base station, which allocates buffers to handoff/new calls based on certain criteria. Buffers at the base station are divided into two categories: handoff call and new call buffers. For handoff calls, network will first make an attempt to allocate handoff calls within handoff buffers, if not available, it will try to allocate from new call buffers. Decision to fix handoff call buffers is decided by the cell administrator based on history of call arriving into a cell from other cells. Some of the users may move out of the cell thus releasing buffers, which can be used for other requesting applications. APPLICATION REQUEST BUFFER ACCEPT/REJECT ALLOCATOR APPLICATION DATABASE FUZZY ALLOCATION Figure 1: Buffer allocation model 2.3. Fuzzy controlled buffer allocation Fuzzy controlled buffer allocation is shown in figure 2, which consists of fuzzification, inference, and defuzzification steps. PRIORITY PACKET SIZE FUZZIFIER INFERENCE RATE OF BUFFER DEFUZZIFIER ALLOCATION FACTOR FLOW 2.2. Buffer allocation The proposed fuzzy based buffer allocation is shown in figure 1 which is located at the base station, which comprises of buffer allocator, application database and fuzzy allocation scheme. The functions of each block is given below. • Buffer Allocator: it receives application connection request either from handoff/new calls with required specifications such as bandwidth, delays, etc. Based on the type of call it first tries to allocate from relevant buffers (hand off/new) or any of the free buffers if required bandwidth is not available by using fuzzy allocation scheme. If sufficient buffers are allocated to accommodate at least minimum bandwidth requirement of an application, then application will be accepted, otherwise rejected. It updates the application database with call information and the buffers allocated to it. • Fuzzy allocation: this calculates the allocation factor for requesting application and sends the calculated allocation factor to the buffer allocator to efficiently allocate the buffer. Care is taken that handoff and real-time calls are given highest preference than any other type of call. • Application database: this comprises of information of all existing calls such as bandwidth required, bandwidth allocated, buffers allocated, buffer allocation factor, type of call, etc. It also comprises of cell status such as bandwidth available, buffers available in both handoff and new call categories. RULE BASE Figure 2: Fuzzy buffer allocation factor computation In the fuzzification step, fuzzy parameter values are converted into linguistic values (such as low, high or medium). Each fuzzy set is associated with a membership function used to characterize how certain the crisp input belongs to the set. For a given crisp input, the membership function returns a real number in the range [0,1]. The closer the membership value is to 1, the more certain the input belongs to the set. Fuzzy inputs considered in the proposed work are priority, packet size and rate of flow. A single crisp value can take more than one linguistic value if the membership values overlap. In the inference step, a set of rules called rule-base, which emulates the decision-making process of a human expert is applied to the linguistic values of the inputs to infer the output sets which represents the actual control signal for the process. We refer the reader to [9] for more complete background information on the fuzzy control. 2.4. Fuzzification Fuzzy based buffer allocation scheme considers three parameters for fuzzification: priority of the application (P), packet size of each application (PS) and the rate of flow of each application (R). The output of linguistic parameters is the buffer allocation factor for a given application. The membership to each of fuzzy variables is assigned using intuition method. For each of the considered fuzzy parameter, their range of linguistic values are depicted in figure 3. LOW MEDIUM HIGH priority priority P0 P1 SMALL P2 P3 MEDIUM P4 LARGE packet size ps0 ps1 LOW ps2 ps3 ps4 HIGH MEDIUM rate flow HG HI HG HG HG HG HG HI HI MD MD MD HG LW HG HG ME LW LW HI ME HI ME r1 r2 r3 MEDIUM LOW r4 HIGH buffer allocation factor bf0 bf1 bf2 bf3 bf4 Figure 3: Membership function for input and output linguistic parameters 2.5. Inference and defuzzification Since there are three linguistic values P , P S and R, the total number of rules is 27. If the condition is true, we call the rule as being active. In our case, the rule-base is in a form called functional fuzzy system where each rule i is written as follows. Rule i:IF P is low and R is low and P S is small,THEN AF = low Where AF linguistic value is decided based on membership functions of three input fuzzy parameters, priority, packet size and flow rate. To decide an appropriate AF , the strength of each rule must be considered. For this reason, the output membership function is a complicated function and center of area method [9] is used for defuzzification. This method finds the center point of the fuzzy output membership function which is used for allocating buffer for requesting application. The fuzzy rule base with 27 rules is shown in figure 4. The defuzzified output parameter will give flexibility to the network administrator to perform soft buffer allocation. Packet size Allocation factor LR MI H S LR MI S LR MI S H M M M M M M LR M MI M HI S M ME MD LR M ME MD MI M ME ME ME MD LW S M ME LW HI HI LR MI S L L L M M LO LO LO r0 Rate of flow LW H HI LR MI S LO MD LR M L LO MD MI L LO S L LO LO MD LW LW LR MI L L LO LW S L PRIORITY (HG=HIGH,ME=MEDIUM,LO=LOW) RATE OF FLOW(LW=LOW,MD=MEDIUM,HI=HIGH) PACKET SIZE (SMALL=S,MI=MEDIUM,LR=LARGE) ALLOCATION FACTOR(L=LOW,M=MEDIUM,H=LARGE) Figure 4: Fuzzy rule base table buffers reserved for handoff calls and y% for new calls generated within the cell. Algorithm 1: Buffer allocation in a base station {Nomenclature: n= number of requesting applications, BW max= Maximum bandwidth,BW req= Bandwidth requested (in Mbps), Bav= Available bandwidth, Bmax=Maximum buffer size at the base station, N ormal Buf f =y*Bmax, Handof f Buf f =x*Bmax, i=ith running application, t = time for buffering, Breq= Buffer required in one second, BALLOC = Buffers allocated, AF = Allocation factor, SU M = Buffers allocated for existing calls within the base station, SU M 1= Buffers allocated for handoff calls.} Begin 1. Receive the application request with required bandwidth, BWreq; 2. If (BWreq ≤ Bav) then bandwidth is allocated, Else compute the buffer required, which is equal to, Breq = (BWreq - Bav)*t; 3. Call Algorithm 2 to compute buffer allocation factor (AF); 4. For new calls perform the following 2.6. Algorithm • BALLOC= Breq * AF; This section presents pseuocode (Algorithms 1 and 2) for the working of the proposed scheme. We consider x% • If BALLOC ≥ (Normal buff - SUM) then reject the new call; • If BALLOC ≤ (Normal buff - SUM) then allocate the buffers and update the application database with allocated buffers and increment SUM by BALLOC; inform the application; 5. For handoff calls perform the following • BALLOC= Breq * AF; • If BALLOC ≥ (Handoff buff - SUM1) then allocate from Normal buff if buffers available, otherwise, reject the handoff call; • If BALLOC ≤ (Handoff buff - SUM1) then allocate the buffers and update the application database with allocated buffers and increment SUM1 by BALLOC; inform the application; 6. Stop End. Algorithm 2: Computation of allocation factor Begin BWreq1=3, BWreq2=5, pb=0.2, Bmax=100 and 200 for different cases, Bn=80%, Bh=20%, r0=1.0, r1=1.25, r2=1.5, r3=1.75, r4=2.0, ps0=500, ps1=750, ps2=1000, ps3=1250, ps4=1500, bf0=0.25, bf1=0.45, bf2=0.65, bf3=0.80, bf4=1.0. Simulation procedure is as follows. Begin 1. Generate a cellular network. 2. Generate the application/call requests. 3. Apply the proposed scheme. 4. Compute the performance of the system. End The performance parameters measured are as follows. • Buffer utilization: It is defined as the ratio of buffer utilized to the maximum size of buffer available at base station. 1. Initialize fuzzy controller with priority of a application, rate of flow of data, packet size of a application; • Handoff Calls Accepted: It is defined as the ratio of handoff calls accepted to the total handoff calls arrived. 2. Find the membership function of priority, rate of flow and packet size allocated to application; • New Calls Accepted: It is defined as the ratio of new calls accepted to the total new calls generated. 4. Inform AF value to buffer allocator; 5. Return; 6. Stop; End. 3. Simulation This section describes the simulation model, simulation procedure and performance parameters. Simulation is being carried out on a pentium-4 machine by using C programming language. A single cell environment with an area of (x, y) meters is considered. n number of users are generated in a cell comprising of both handoff and new calls. Maximum bandwidth of a cell is assumed to be BW max Mbps. Bandwidth requests for each call are generated randomly in the range BW req1 to Bred2 Mbps. Calls are randomly categorized into handoff and new calls by using a probability pb, i.e., if generated random number (0 to 1) is less than pb, then it is called as handoff call. Maximum Buffer Size at the base station of the cell is assumed to be Bmax Mbytes . Maximum Buffer size is divided into two regions, Normal Region= Bn%of Bmax MBytes and Handoff Region= Bh%of Bmax MBytes. Following inputs are considered for simulation. x=500, y=1000, n is varied from 5 to 50, BWmax=20, 3.1. Results The results that we will present here demonstrate the capability of our fuzzy buffer allocation algorithm to allocate the buffer in worst case. Also, we compare the fuzzy based buffer allocation scheme with non fuzzy based buffer allocation scheme. A non fuzzy based buffer allocation scheme performs static buffer allocation based on the absolute buffer requirements besides considering random early expiration detection for packet dropping. From figure 5, we observe that buffer utilization for different capacity of buffers (100 and 200 MB) as compared to non fuzzy method is better. This is because of soft buffer allocation based on application type. % of buffer utilization .Vs. Number of calls 1 0.9 % of buffer utilization 3. Compute AF by referring to rule base; 0.8 0.7 0.6 0.5 Fuzzy (100 MB) 0.4 Non fuzzy (100MB) Fuzzy (200 MB) Non fuzzy (200MB) 0.3 0.2 0.1 5 10 15 20 25 30 35 40 Number of calls Figure 5: Buffer utilization (%) Vs. Call arrival rate It is seen that fuzzy based scheme has better acceptance of handoff calls for both the cases of buffer capacity as depicted in figure 6. 5. Acknowledgment % of accepted handoff calls .Vs. Number of handoff call arrivals We are thankful to All India Council of Technical Education, New Delhi, and National Project Implementation Unit, New Delhi, for sponsoring the part of the project under Research Promotion Scheme (8023/RID/BOR/RPS-43/2005-06) and TEQIP, respectively. % of accepted handoff calls 1 0.9 0.8 0.7 0.6 Fuzzy (100 MB) Non fuzzy (100MB) Fuzzy (200 MB) Non fuzzy (200MB) 0.5 0.4 0.3 2 4 6 6. References 8 10 12 14 16 Number of handoff call arrivals Figure 6: Handoff calls accepted (%) Vs. Number of handoff call arrivals Figure 7 shows that fuzzy based scheme performs better in acceptance of new calls for both the cases of buffer capacity. % of accepted new calls .Vs. Number of new calls arrivals % of accepted new calls 0.9 0.8 0.7 0.6 Fuzzy (100 MB) Non fuzzy (100MB) 0.4 Fuzzy (200 MB) Non fuzzy (200MB) 0.3 0.2 5 10 15 Number of new calls arrivals 20 [1] Jian Chen, Victor C.M. Leung, ”Applying active queue management to link layer buffers for real-time traffic over third generation wireless networks”, Proc. IEEE Wireless Communications and Networking, New Orleans, USA, vol. 3, pp. 1657-1662, March 2003. [2] Yuan Chen, Lemin Li, ”A Random Early Expiration Detection Based Buffer Management Algorithm for Real-time Traffic over Wireless Networks”, Proc. 5th International Conference on Computer and Information Technology (CIT’05), Shangai, China, 2005 [3] Sang-Ho Lee, Kyu-Young Whang, Yang-Sae Moon, Wook-Shin Han, Il-Yeol Song, ”Dynamic buffer allocation in video-on-demand systems”, IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 6, pp. 1535 - 1551, 2003. 1 0.5 such as defense calls, VIP calls, etc., apart from the realtime and non real-time characteristics of an application. 25 Figure 7: New calls accepted (%) Vs. Number of new call arrivals 4. Conclusions In this paper, we proposed a scheme for buffer allocation of multimedia applications by using fuzzy logic. The main objective is to use the base station buffers efficiently and decrease the call rejections especially for handoff calls. One important characteristic of our fuzzy based buffer allocation scheme is that allocation is done looking at some fuzzy parameters of each application. Parameters considered are priority, rate of flow, and packet size. Extensive simulation results reveal that our scheme features low call rejecting probability, and good buffer utilization as compared to a traditional buffer allocation scheme. The scheme can be extended to perform buffer allocation by considering the criticality of an application [4] Jingxuan Liu, Ansari, ”Class-based dynamic buffer allocation for optical burst switching networks”, Proc. Workshop on High Performance Switching and Routing, Merging Optical and IP Technologies, Torino, Italy, pp. 295-299, 2002. [5] Yongho Seok, Jaewoo Park, Yanghee Choi, ”Queue Management Algorithm for multi-rate wireless local area networks”, Proc. 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, Anchorage, Alaska, USA, vol.3, pp. 2003-2008, 2003. [6] Mats Sagfors, Reiner Ludwig, Michael Meyer, Janne Peisa, ”Buffer Management for Rate-Varying 3G Wireless Links Supporting TCP Traffic”, Proc. Vehicular Technology Conference, vol. 1, pp. 675-679, 2003. [7] Janaki Bandara, ”Resource Allocators for Non Real Time Traffic in Wireless Sensor Network using Fuzzy Logic”, International Journal on Wireless Personal Communications, vol. 21, no. 3, pp. 329-344, June 2002. [8] T. J. Ross, Fuzzy Logic for Engineering Applications, 1998