Buffer Management Algorithm Design and Implementation Based on Network Processors

Transcription

1 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, Buffer Management Algorthm Desgn and Implementaton Based on Network Processors Yechang Fang, Kang Yen Dept. of Electrcal and Computer Engneerng Florda Internatonal Unversty Mam, USA {yfang003, Deng Pan, Zhuo Sun School of Computng and Informaton Scences Florda Internatonal Unversty Mam, USA {pand, Abstract To solve the parameter senstve ssue of the tradtonal RED (random early detecton) algorthm, an adaptve buffer management algorthm called PAFD (packet adaptve far droppng) s proposed. Ths algorthm supports DffServ (dfferentated servces) model of QoS (qualty of servce). In ths algorthm, both of farness and throughput are consdered. The smooth buffer occupancy rate functon s adopted to adjust the parameters. By mplementng buffer management and packet schedulng on Intel IXP2400, the vablty of QoS mechansms on NPs (network processors) s verfed. The smulaton shows that the PAFD smoothes the flow curve, and acheves better balance between farness and network throughput. It also demonstrates that ths algorthm meets the requrements of fast data packet processng, and the hardware resource utlzaton of NPs s hgher. Keywords-buffer management; packet droppng; queue management; network processor I. INTRODUCTION Network nformaton s transmtted n the form of data flow, whch consttutes of data packets. Therefore, dfferent QoS means dfferent treatment of data flow. Ths treatment nvolves assgnment of dfferent prorty to data packets. Queue s actually a storage area to store IP packets wth prorty level nsde routers or swtches. Queue management algorthm s a partcular calculaton method to determne the order of sendng data packets stored n the queue. Then the fundamental requrement s to provde better and tmely servces for hgh prorty packets [1]. The NP s a dedcated processng chp to run on hgh speed networks, and to acheve rapd processng of packets. Queue management plays a sgnfcant role n the control of network transmsson. It s the core mechansm to control network QoS, and also the key method to solve the network congeston problem. Queue management conssts of buffer management and packet schedulng. Generally the buffer management s appled at the front of a queue and cooperates wth the packet schedulng to complete the queue operaton [2, 3]. When a packet arrves at the front of a queue, the buffer management decdes whether to allow the packet comng nto the buffer queue. From another pont of vew, the buffer management determnes whether to drop the packet or not, so t s also known as droppng control. The control schemes of the buffer management can be analyzed from two levels, data flow and data packet. In the data stream level and vewed form the aspect of system resource management, the buffer management needs to adopt certan resource management schemes to make a far and effectve allocaton of queue buffer resources among flows through the network nodes. In the data packet level and vewed from the aspect of packet droppng control, the buffer management needs to adopt certan drop control schemes to decde that under what knd of crcumstances a packet should be dropped, and whch packet wll be dropped. Consderng congeston control response n an end-to-end system, the transent effects for droppng dfferent packets may vary greatly. However, statstcs of the long-term operaton results ndcates that the transent effect gap s mnmal, and ths gap can be neglgble n majorty of cases. In some specfc crcumstances, the completely shared resource management scheme can cooperate wth drop schemes such as tal-drop and head-drop to reach effectve control. However, n most cases, nteracton between these two schemes s very large. So the desgn of buffer management algorthms should consder both of the two schemes to obtan better control effects [4, 5]. 1

2 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, II. EXISTING BUFFER MANAGEMENT ALGORITHMS that QoS of servce flows wth poor transmsson condtons Reference [6] proposed the RED algorthm for actve queue management (AQM) mechansm [7] and then standardzed as a recommendaton from IETF [8]. It ntroduces congeston control to the router's queue operatons. RED uses early random drop scheme to smooth packet droppng n tme. Ths algorthm can effectvely reduce and even avod the congeston n network, and also solve the TCP protocol global synchronzaton problem. cannot be guaranteed. Packet schedulng algorthms usually use generalzed processor sharng (GPS) as a comparatve model of farness. Durng the process of realzaton of packet schedulng algorthms based on GPS, each servce flow has been assgned a statc weght to show ther QoS. The weght actually express the percentage of the servce flow n the entre bandwdth B. wll not change wth packet schedulng algorthms, and meet However, one concern of the RED algorthm s the stablty problem,.e., the performance of the algorthm s very senstve to the control parameters and changes n network traffc load. Durng heavy flow crcumstances, the performance of RED wll drop drastcally. Snce RED algorthm s based on best-effort servce model, whch does not consder dfferent levels of servces and dfferent user flows, t cannot provde farness. In order to mprove the farness and stablty, several mproved algorthms have been developed, ncludng WRED, SRED, Adaptve-RED, FRED, RED wth In/Out (RIO) [9, 10] etc. But these algorthms stll have a lot of problems. For example, a large number of studes have shown that t s dffcult to fnd a RIO parameter settng sutable for varous and changng network condtons. III. THE PAFD ALGORITHM In ths paper, we propose a new buffer management algorthm called PAFD (Packet Adaptve Far Droppng). Ths algorthm wll adaptvely gan balance between congeston and farness accordng to cache congeston stuaton. When there s mnor congeston, the algorthm wll tend to farly drop packets n order to ensure all users access the system resources to ther scale. For moderate congeston, the algorthm wll nclne to drop the packet of low qualty servce flows by reducng ts sendng rate usng schedulng algorthm to allevate congeston. In severe congeston, the algorthm wll tend to farly drop packets, through the upper flow control mechansm to meet the QoS requrements, and reduces sendng rate of most servce flows, n order to speed up the process of easng the congeston. In buffer management or packet schedulng algorthms, t wll mprove the system performance to have servce flows wth better transmsson condtons reserved n advance. But ths operaton wll make system resources such as buffer space and bandwdth be unfarly dstrbuted, so N 1 (1) 1 where N expresses the number of servce flows n the lnk. And the servce volume s descrbed by g nc B (2) jb where, j denotes two dfferent servce flows. In GPS based algorthms, the bandwdth allocaton of dfferent servce flows meets the requrement B / = B j / j, where B s the allocated bandwdth of the servce flow. By assgnng a smaller weght to an unmportant background servce flow, the weght of servce flow wth hgh prorty hgh wll be much larger than low, so that the majorty of the bandwdth s accessed by hgh-prorty servce flows. A. Algorthm Descrpton In buffer management algorthms, how to control the buffer space occupaton s very key [11]. Here we defne j j C j (3) W C W where C s the buffer space occupaton, and W expresses the synthetc weght of the servce flow. When the cache s full, the servce flow wth the largest value of C /W wll be dropped n order to guarantee farness. Here the farness s reflected n packets wth dfferent queue length [12, 13]. Assume that u s the weght, and v s the current queue length of the servce flow. The synthetc weght W can be calculated as descrbed by W u (1 ) v (4) where s the adjust parameter of the two weghtng coeffcents u and v. can be pre-assgned, or determned n accordance wth usage of the cache. u s related to the 2

3 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, servce flow tself, and dfferent servce flows are assgned cyclng tmes s related to the rato between the longest and wth dfferent weght values. As long as the servce flow s the shortest packets. At ths moment, the tme complexty actve, ths factor wll reman unchanged. v s tme varyng, overhead s stll small based on practces. whch reflects droppng stuaton of the current servce flow. In Step 2,, a functon of shared buffer, s a parameter Suppose a new packet T arrves, then the PAFD for adjustng proporton of the two weghtng coeffcents u algorthm process s descrbed as follows: and v. For a large value of, the PAFD algorthm wll tend to farly select and drop packets accordng to the synthetc Step 1: Check whether the remanng cache space weght W. Otherwse, the algorthm tends to select and drop can accommodate the packet T, f the remanng the servce flow wth large queue length. A reasonable value space s more than or equal to the length of T, add T for can be used to balance between farness and nto the cache queue. Otherwse, drop some packets performance. Here we ntroduce an adaptve method to from the cache to free enough storage space. The determne the value of. Ths adaptve method wll decson on whch packet wll be dropped s gven n determne value based on the congeston stuaton of the the followng steps. cache, and ths process does not requre manual nterventon. Step 2: Calculate the weghtng coeffcents u and v When there s a mnor congeston, the congeston can be for each servce flow, and the parameter. Then get releved by reducng the sendng rate of a small number of the values of new synthetc weghts W for each flow servce flows. The number of servce flows n wreless accordng to (4). network nodes s not as many as that n the wred network. Step 3: Choose the servce flow wth the largest So the mnor congeston can be releved by reducng the weghted buffer space occupaton (C /W ), f the sendng rate of any one of servce flows. We hope ths servce flow assocated to the packet T has the same choce s far, to ensure that all user access to the system value as t, then drop T at the probablty P and resources accordng to ther weghts. returns. Otherwse, drop the head packet of the When there s a moderate congeston, the congeston can servce flow wth the largest weghted buffer space not be releved by reducng the sendng rate of any one of occupaton at probablty 1P, and add T nto the servce flows. Reducng the rate of dfferent servce flows cache queue. Here Probablty P s a random number wll produce dfferent results. We hope to reduce the rate of generated by the system to ensure the smoothness servce flows whch are most effectve to the relef of and stablty of the process. congeston. That s, the servce flow whch current queue Step 4: Check whether the remanng space can length s the longest (The tme that these servce flow accommodate another new packet, f the answer s occuped the cache s also the longest). Ths not only yes, the packet wll be transmtted nto the cache. mproves system throughput, but also made to speeds up the Otherwse, return to Step 3 to contnuously choose congeston relef. and drop packets untl there s suffcent space. When there s a severe congeston, t s obvous that If all packet lengths are the same, the algorthm only reducng the sendng rate of a small porton of the servce needs one cycle to compare and select the servce flow wth flows cannot acheve the congeston relef. We may need to the largest weghted buffer space occupaton. Therefore, the reduce the rate of a lot of servce flows. Snce the TCP has a tme complexty of the algorthm s O(N). In ths case, we characterstc of addtve ncrease multplcatve decrease also need addtonal 4N storage space to store the weghts. (AIMD), contnuous drop packets from one servce flow to Takng nto account the lmted capacty of wreless network, reduce the sendng rate would adversely affect the N s usually less than 100. So n general the algorthm's performance of the TCP flow. Whle the effect on relevng overhead on tme and space complexty are not large. On the system congeston wll become smaller, we gradually other hand, f packet lengths are dfferent, then t s ncrease the values of parameters, and the algorthm wll necessary to cycle Step 3 and Step 4 untl the cache has choose servce flows to drop packet farly. On one hand, at enough space to accommodate the new packet. The largest ths pont the "farness" can brng the same benefts as n the 3

4 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, mnor congeston system; on the other hand ths s to avod In the DffServ model, we retan the mplement process contnuously droppng the longer queue servce flow. of PAFD, and only modfy (4) nto Congeston s measured by the system buffer space occupaton rate. s a parameter relevant to system congeston status and ts value s between 0 to 1. Assume that the current buffer space occupaton rate s denoted by Buffer cur, and Buffer medum, Buffer mn, and Buffer max represent threshold value of the buffer space occupaton rate for moderate, mnor, and severe congeston, respectvely. When Buffer cur s close to Buffer mn, the system enters a state of mnor congeston. When Buffer cur reaches Buffer max, the system s n a state of severe congeston. Buffer medum means moderate congeston. If we value by usng lnear approach, the system wll have a dramatc oscllaton. Instead we use hgh order nonlnear or ndex reducton to get smooth curve of as shown n Fgure 1. W u (1 ) v ) (6) ( where s a new parameter used to adjust the farness among servce flows of dfferent servce levels. As mentoned above, we can set the value of parameter dfferent from that shown n Fgure 1 to satsfy dfferent requrements. s the parameter whch balances farness and transmsson condtons. For hgh-prorty servces, the curve n Fgure 1 s reasonable. The farness s able to guarantee the QoS for dfferent servce flows, and also s requred to relef congeston quckly. For hgh-prorty servces whch have no delay constrants and hgh farness requrements, a hgher throughput s more practcal. Therefore, we can get the value of the parameter for low-prorty servces, whch s slghtly less than that for hgh-prorty servces as shown n Fgure 2. Fg.1. An adaptve curve of The value of can also be calculated as below 2 2 0, f Buffercur < Buffermn 1, f 2 2 1, f Buffercur < Buffermax 2 2 Buffercur Buffermn Buffer 2 2 mn Buffer cur Buffer max Buffer max Buffer mn (5) B. DffServ Model Support In the PAFD algorthm, we can adopt the DffServ model to smplfy the servce flows by dvdng them nto hgh-prorty servces such as assurance servces and low-prorty servces such as best-effort servces. We use the queung method for the shared cache to set and manage the cache. When a new packet arrves at the cache, frst the servce flow s checked to see whether t matches the servce level agreement (SLA). If t does, then ths new packet enters the correspondng queue. Otherwse, the packet s assgned to low-prorty servces, and then enters the low-prorty queue. Fg.2. Values of for dfferent prorty servces Now we check the effects of the parameter. For hgh-prorty servces, s a constant wth value 1. For low-prorty servces, the value of s less than 1, and nfluenced by the network load. When network load s low, equals to 1. In ths case, dfferent level servce flows have the same prorty to share the network resources. As network load ncreases, n order to guarantee the QoS of hgh-prorty servces, low-prorty servces gradually gve up some transmsson opportuntes, so the value of decreases. The 4

5 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, hgher network load s, the smaller the values of and W are. channel transmsson condton wll gve hgher prorty and Therefore, the probablty of a low-prorty packet beng result effectve throughput. dropped s hgher. Values of are shown below. Fg.3. Values of for dfferent prorty servces IV. SIMULATION RESULTS A. Smulaton for Commen Servces We compare the PAFD algorthm wth two commonly used buffer management algorthms RED and tal drop (TD). We choose two common packet schedulng algorthms Best Channel Frst (BCF) and Longest Queue Frst (LQF) to work wth PAFD, RED and TD. Here the LQF uses the weghted queue length for packet schedulng. So there are 6 queue management algorthm combnatons, whch are PAFD-BCF, PAFD-LQF, RED-BCF, RED-LQF, TD-BCF, and TD-LQF. The performance comparsons of these algorthms are carred out wth respect to throughput effectveness, average queung delay, and farness. We use K1297-G20 sgnalng analyzer to smulate packet sendng, and the operaton system for K1297-G20 s Wndows NT 4.0. ADLINK 6240 s used as the NP blade. Based on the smulaton confguraton, there are 8 dfferent packet length confguratons for the data source. They are fxed length of 64 bytes, fxed length of 65 bytes, fxed length of 128 byte, fxed length of 129 bytes, fxed length of 256 bytes, random length of bytes, random length of bytes, and random length of bytes. Fgure 4 shows that all the algorthms have smlar throughputs for low network load. When the load ncreases, the throughput effectveness of BCF s hgher than that of other schedulng algorthms. Ths fgure shows that PAFD-BCF provdes sgnfcant hgher throughput than the other algorthms. PAFD does not randomly drop or smply tal drop packets, but fully consders farness and transmsson condtons. In ths way, servce flows under poor transmsson condton receve hgh probablty of packet droppng, thus a relatvely short vrtual queue. When BCF s workng wth PAFD, the servce flow under better Fg.4. Throughputs of RED and PAFD Fg.5. Average Queung Delay for TD, RED and PAFD From Fgure 5, we fnd that RED has better performance on the average queung delay due to the capablty of early detecton of congeston and ts drop mechansm. BCF has better performance on queung delay than that of LQF. As the load ncreases, the average queung delay of PAFD frst ncreases, then decreases. Ths s because RAFD does not use tal drop, and nstead searches a servce flow wth the largest weghted buffer space occupaton to drop the head packet to reduce the average queung tme. Both TD and RED use shared cache nstead of flow queung so that they fal to consder the farness. Here the farness ndex F s gven by N G 2 ( ) 1 W F N G N ( ) 1 W where G s the effectve throughput of servce flow, and N 2 (7) 5

6 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, s the total number of servce flows. It s not dffcult to prove that F(0, 1). When F has a lager value, the farness of the system s better. If the value of F equals to 1, the system resource s completely far. We can use (7) to calculate the farness ndex and compare the farness of dfferent algorthms. In ON-OFF model wth the assumpton that there are 16 servce flows, the ON average rate of flows 1-8 s twce of that of That s, W : W j = 2 : 1, where [1, 8] and j[9, 16]. Usng round robn algorthms wthout consderng W, we can calculate the reference value of farness ndex F = 0.9. Table I gves the farness ndex of TD, RED and PAFD whch are combned wth packet Fg.6. Throughputs of RED and DS-PAFD schedulng algorthms. Algorthms TABLE I. FAIRNESS INDEX Farness TD-BCF TD-LQF RED-BCF RED-LQF PAFD-LQF PAFD-BCF The table ndcates that the farness ndex of BCF s lower when combned wth TD and RED. Snce PAFD takes the farness nto consderaton, the farness ndex of PAFD s hgher than that of TD when there are congestons. The combnaton of PAFD and LQF has hgher throughput and more far dstrbuton of cache and bandwdth resources. By changng the value of parameter, we can convenently balance the system performance and farness based on the requrements. Fg.7. Average Queung Delay of RED and DS-PAFD Table II gves the comparson of farness ndex of theses algorthms. Comparng these numbers wth those shown n Table I, we can draw a smlar concluson. However, the dfference n values s that the farness ndex of low-prorty servces s slghtly lower than that of hgh-prorty servces as a result of dfferent values of parameter selected. B. Smulaton for DffServ Model TABLE II. COMPARISON OF FAIRNESS INDEX In ths secton we adopt the same envronment as descrbed n the prevous secton to test the PAFD performance based on the DffServ model. The only dfference s that half of the servces are set to hgh-prorty, and another half to low-prorty. Fgures 6 and 7 show the throughput and average queung delay of those algorthms. The only dfference n these two tests s that the value of parameter for half of the servce flows used n the second smulaton s slghtly lower than the one n the frst smulaton. So the curves n Fgures 7 and 8 are very smlar to those shown n Fgures 4 and 5. TD-BCF TD-LQF Flow DSPAFD-BCF DSPAFD-LQF Hgh-prorty Servce Flow DSPAFD-BCF DSPAFD-LQF Low-prorty Servce Flow As shown n Fgures 2-3, 6 and 7, when network load s lght, the throughputs are smlar for dfferent prorty servces. Ths means dfferent prorty servces have the same prorty to share network resources. As network load 6

7 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, ncreases, the throughput gradually decreases. However, even n the case of heavy load, the PAFD algorthm stll are 1024 queues n total. As we adopt the SRAM structure, t s very easy to enqueue. allocates small porton of resources to low-prorty servces The dequeung operaton s smlar to the enqueung to meet the farness requrement. And ths operaton wll operaton. In order to mantan the performance of the prevent hgh-prorty servces from fully occupyng the system, mcro engne threads of NPs must operate n strct network resources. accordance wth the predetermned sequence. Ths s V. IMPLEMENTATION BASED ON NETWORK PROCESSORS controlled by nternal thread semaphore. When a queue changes from empty to non-empty n an enqueung Here we adopt NP Intel IXP2400 to mplement the operaton, or from non-empty to empty n a dequeung PAFD algorthm. Intel IXP2400 provdes us wth eght operaton, the buffer manager of PAFD wll send a message mcro-engnes, and each mcro-engne can support up to to packet schedulng module through the adjacent loop. eght hardware threads. When the system s runnng, each mcro-engne deals wth one task. Durng the thread VI. CONCLUSIONS swtchng, there s no need for protecton, each hardware Buffer management algorthm s the core mechansm to thread has ts own regster, so the swtchng speed s very acheve network QoS control. It also plays an mportant role fast. Also Intel IXP2400 s approprate for DffServ model. n network resource management. In ths paper, a novel The PAFD Algorthm executes enqueung and dequeung operatons n the transmsson, whch are mplemented usng chaned lst of the SRAM of IXP2400. The buffer manager of PAFD receves enqueung request from the functonal ppelne, and accepts dequeung request through the mcro engnes of NPs. In the PAFD algorthm, Q-Array n the SRAM controller s used to the chaned lst, and a queue descrptor s stored n the SRAM. The buffer manager uses content assocatve memory (CAM) to mantan queue buffer of the descrptor. When enqueung request arrves, the buffer manager wll check CAM to see f the queue buffer management algorthm called PAFD s proposed based on NPs. The PAFD algorthm takes nto account the mpact of transmsson envronment on packets. It can adaptvely balance between queue congeston and farness accordng to cache congeston. PAFD also supports the DffServ model to mprove network QoS based on NPs. The smulaton results show that the throughput and farness are better balanced after ths algorthm s appled. Fnally, the PAFD algorthm s mplemented based on IXP2400, whch means that the hardware resource utlzaton of NPs s hgher. descrptor s n the local buffer. If so, PAFD wll be run to The future network has two development requrements: decde whether the new packets should enter the queue. If hgh-speed bandwdth and servce dversfcaton. Research not, the descrptor s excluded from the Q-Array, and then on buffer management algorthms s able to sut for these stored n the SRAM. Therefore, another specfed queue requrements. In the future, buffer management wll become descrptor s read nto the Q-Array, and then PAFD s run to more complex. Therefore, the requrements for NPs and decde whether to drop the new packets. When a queue other hardware wll be more strngent. It s very mportant to enters a queue, Q-Array logc moves the frst four bts to the consder the comprehensve performance of the algorthms SRAM controller. Q-Array can buffer 64 queue descrptors whle pursung smplcty and easy mplementaton. n each SRAM channel. The PAFD algorthm only reserves 16 entrances for the buffer manager, and the rest are for free dle chaned lst and SRAM loops. The current count of packets s stored n the local memory. Ths operaton needs 16 bts, and each bt represents the number of packets through the 16 entrances. The packet counter s ntaled ACKNOWLEDGEMENTS Ths work was supported by Presdental Fellowshp and Dssertaton Year Fellowshp , Florda Internatonal Unversty. REFERENCES when entrances are read nto the Q-Array, and then t [1] Intel Corporaton, Intel nternet exchange archtecture software buldng blocks developer s manual [M/ CD], Document Number: executes the operaton of plus one or mnus one base on the : , 73-86, response. The mplemented system we desgned supports 64 vrtual ports, and each port supports 16 queues. Thus, there [2] F. Buccafurr et. al., Analyss of QoS n cooperatve servces for real tme applcatons, Data & Knowledge Engneerng, Vol.67, No.3,

8 (IJCSIS) Internatonal Journal of Computer Scence and Informaton Securty, [3] Yoshhro Ito, Shuj Tasaka, Feasblty of QoS control based on QoS Deng Pan receved hs Ph.D. and M.S. mappng over IP networks, Computer Communcatons, Vol.31, No.10, degree n Computer Scence from State [4] Anunay Twara and Anrudha Sahoo, Provdng QoS n OSPF based best effort network usng load senstve routng, Smulaton Modellng Practce and Theory, Vol.15, No.4, Unversty of New York at Stony Brook n 2007 and He receved M.S. and B.S. n Computer Scence from X'an Jaotong [5] Danel A. Menascéa, Hongle Ruana, and Hassan Gomaa, QoS Unversty, Chna, n 2002 and 1999, management n servce-orented archtectures, Performance Evaluaton, Vol.64, No.7, respectvely. He s currently an Assstant [6] S. Floyd, V. Jacobson, Random Early Detecton Gateways for Congeston Avodance, IEEE/ACM Transactons on Networkng Professor n the School of Computng and Informaton Scences, FIU. He was an [7] (TON), August, Assstant Professor n School of Computng and Informaton Scences, FIU Nabeshma, Masayosh, Improvng the performance of actve buffer from 2007 to Hs research nterests nclude hgh performance routers management wth per-flow nformaton, IEEE Communcatons Letters, Vol.6, No.7, July, and swtches, hgh speed networkng, qualty of servce, network processors [8] RFC: Recommendatons on Queue Management and Congeston Avodance n the Internet. and network securty. [9] W. Feng, Kang G. Shn, D.D. Kandlur, and D. Saha, The Blue actve Zhuo Sun receved her BS degree n queue management algorthms, IEEE/ACM Transactons on Networkng Vol.10, No.4, pp , computer scence from Guangx [10] C.V. Hollot, V. Msra, D. Towsley, and W. Gong, Analyss and desgn of controllers for AQM routers supportng TCP flows, IEEE Transactons on Automatc Control, Vol.47, No.6, pp , Unversty, Nannng, Chna, n 2002, and the MS degree n software engneerng from Sun Yat-sen Unversty, Guangzhou, [11] M. Ghader and R. Boutaba, Call admsson control for voce/data Chna, n Then she worked at Nortel ntegraton n broadband wreless networks, IEEE Transactons on Moble Computng, Vol.5, No.3, Guangzhou R&D, Guangzhou, Chna. She [12] G. Asca, V. Catana, D. Panno, An Effcent Buffer Management s currently a second year Ph.D student n Polcy Based On an Integrated Fuzzy-Ga Approach, Proc. of IEEE Florda Internatonal Unversty. Her INFOCOM 2002, New York, USA, Jun research nterests are n the areas of [13] Ellen L. Hahne, Abhjt K. Choudhury, Dynamc queue length hgh-speed network. thresholds for multple loss prortes, IEEE/ACM Transactons on Networkng, Vol.10, No.3, June AUTHORS PROFILE Yechang Fang receved hs M.S. n Electrcal Engneerng from Florda Internatonal Unversty (FIU), Mam, USA n From 2006 to 2007, he served as an IT specalst at IBM Chna to work wth Noka, Motorola and Ercsson. He s currently a Ph.D. canddate wth a Dssertaton Year Fellowshp n the Department of Electrcal and Computer Engneerng, FIU. Hs area of research s telecommuncaton. Besdes, hs research nterests also nclude computer networkng, network processors, fuzzy Logc, rough sets and classfcaton. Kang K. Yen receved the M.S. degree from Unversty of Vrgna n 1979 and Ph.D. degree from Vanderblt Unversty n He s currently a Professor and Char of the Electrcal Engneerng Department, FIU. He s also a regstered professonal engneer n the State of Florda. He has been nvolved n theoretcal works on control theory and on parallel smulaton algorthms development for real-tme applcatons n the past several years. In the same perods, he has also partcpated n several ndustry supported projects on real-tme data processng and mcroprocessor-based control system desgns. Currently, hs research nterests are n the securty related ssues and performance mprovement of computer networks. 8