How to Maximize User Satisfaction Degree in Multi-service IP Networks

Transcription

1 How to Maximize Uer Satifaction Degree in Multi-ervice IP Network Huy Anh Nguyen, Tam Van Nguyen and Deokai Choi Department of Electronic and Computer Engineering Chonnam National Univerity Gwangu, KOREA and Abtract Bandwidth allocation i a fundamental problem in communication network. With current network moving toward the Future Internet model, the problem i further intenified a network traffic demanding far from exceed network bandwidth capability. Maintaining a certain uer atifaction degree therefore become a challenge reearch topic. In thi paper, we deal with the problem by propoing BASMIN, a novel bandwidth allocation cheme that aim to maximize network uer happine. We alo defined a new metric for evaluating network uer atifaction degree: network worth. A three-tep evaluation proce i then conducted to compare BASMIN efficiency with other three popular bandwidth allocation cheme. Throughout the tet, we experienced BASMIN advantage over the other; we even found out that one of the mot widely ued bandwidth allocation cheme, in fact, i not effective at all. Keyword: Bandwidth allocation, network management, utility function, uer happine, network worth. I. INTRODUCTION One fundamental problem in the Internet deign i the allocation and management of network reource. When network reource are limited and traffic load become heavier, uing exiting reource efficiently to enure a certain level of QoS (Quality of Service) become a very important iue. Bandwidth wa long conidered the mot important network reource, and the final goal of reource management i to atify the end uer a bet a we can. Thu drive an intereting reearch topic: How to effectively allocate network bandwidth to maximize network uer atifaction degree? To anwer the quetion above, we will firt try to formulate end-uer happine (or atifaction degree). Each uer of an Internet application derive a certain utility from the network performance. End-uer of the Internet are uually not intereted in how much bandwidth that i available for them, but rather what can they obtain from that amount of bandwidth. It i the main metric that indicate how atified will end-uer be with the network performance. The degree of uer atifaction therefore can be tranlated into ome QoS level by uing a utility function. Shenker wa the firt to define the hape of utility function curve for both elatic and real-time traffic flow in [0]. Thee function relate the allocated bandwidth to end-uer atifaction, rating that atifaction on a 0 to cale. According to Shenker, there are only three type of application on the Internet with three pre-defined utility function. However, thi fact doe not hold in a multi-cla network like the Internet. The utility of a ervice i flexible according to uer ubective perception, and to the requirement of application. Beide, we believe that it i eential to take into account the priority level of different kind of application. It i even poible to define a different utility function to each uer of each Internet application. The precie olution to maximize network utility therefore yield an NP-hard problem [3] and attract the networking reearch community in year. A the Internet evolve from a ingle-ervice data network into the multi-ervice intelligent network in early year of the 2t century, the topic of bandwidth allocation tand out to be one of the mot dynamic reearch topic at thi time, and receive numerou attention from the academic community [3, 6 8]. In 200, Rakocevic [3] propoed the Dynamic Bandwidth Allocation cheme in IP network and Kouik Kar et al. [8] propoed a imple rate control algorithm for maximizing total network uer utility. Due to complexity of the problem, both author eek to a imple olution by uing a heuritic algorithm. However, the reult i not quite atifactory ince they can not be applied to a real network environment yet. Recently, the Internet i moving toward the next generation network model. It i more deeply integrated in our phyical environment with the proliferation of high-peed connection and ubiquitou network. In 2005 and 2006, Zheng Wu in [4] propoed another heuritic approach to maximize uer atifaction degree on multiple MPLS path. Later on, Ning Lu extended the topic to wirele network [], uing claic utility function on IP network to olve the problem of QoS in wirele network. All the above work are evaluated uing NS2 with a imple network model, and the reult are praieworthy. In thi reearch, we try to approach the optimal olution to maximize network uer happine by propoing a new Bandwidth Allocation Scheme for Multi-ervice IP Network (BASMIN). For the ret of thi paper, we will briefly decribe the baic evaluation metric the utility function in Section 2. Section 3 will talk about our network model and problem formulation. Detail of BASMIN will be given in Section 4. Section 5 will preent our recorded imulation reult for the firt evaluation phae. And finally, concluion will be given in Section 6.

2 II. (a) Elatic traffic Figure. UTILITY-BASED ADAPTIVE QOS Utility wa originally ued in economic and ha been brought to networking reearch by Shenker in [0]. It repreent the level of atifaction of a uer or the performance of an application. A utility function here i a curve mapping bandwidth received by application to their performance a perceived by the uer. It i monotonically non-decreaing. In other word, more bandwidth allocated hould not lead to degraded application performance. The key advantage of the utility function i that it can inherently reflect a uer QoS requirement and quantify the adaptability of an application. The hape of utility function varie according to the application characteritic. We aume in thi reearch that any traffic offered to the network belong to three categorie: Elatic application, Hard Real-Time application and Real-Time application. The utility function preented here i a lightly modified verion of Shenker work. A. Elatic application Traditional data application life file tranfer, , remote login and peer-to-peer are rather tolerant of delay. On an intuitive level, they would appear to have decreaing marginal improvement along with incremental increae in bandwidth. Total U will alway be maximized when no uer are denied acce, therefore admiion control ha no role here. For thi type of application, there i no minimum required bandwidth ince it can tolerate relatively large delay. Elatic traffic utility i modeled uing the following function: kb bmax e U () b () e (c) RT traffic (b) HRT traffic Network traffic utility function. where k i a tunable parameter which determine the hape of utility function and enure that when maximum requeted bandwidth i received, U. But a depicted in Fig. (a), uer atifaction of thi application can hardly reach even when provided with a very high bandwidth. Therefore, we believe that bandwidth allocated to thi application type hould never urpa b max, even in the cae that exceive network bandwidth i available. B. Hard-Real Time application Hard Real-Time (HRT) application are the mot delay enitive one. Thee application need their data to arrive within a given delay bound, require trict end-to-end performance guarantee and do not how any adaptive propertie. A network flow belong to thi application type will not be allowed to enter the network if minimum bandwidth requirement cannot be met; and once accepted, allocated bandwidth will be fixed during lifetime of network eion. More bandwidth allocation hould not lead to performance enhancement while any bandwidth degradation will caue QoS (utility) drop to zero. Example include audio/video phone, video conference and telemedicine. Utility function ued for modeling HRT traffic i: kb bmax e U () b (2) e where b max i the bandwidth required. The general hape of HRT traffic utility i depicted in Fig. (b). C. Real-Time application Real-time (RT) application refer to multimedia application that can adapt to variou network load. In cae of congetion, they can gracefully adut their tranmiion rate uch that the QoS i till acceptable. However, thi type of traffic require network to provide a minimum level of performance guarantee. If allocated bandwidth i reduced below ome threhold, QoS will then become unacceptable. Typical example are interactive multimedia ervice, video on demand and online game. The following utility function i ued to model RT traffic: 2 kb k2 b U () b e (3) rt where k and k 2 are tunable parameter which determine hape of the utility function. It can be oberved from Fig. (c) that marginal utility of additional bandwidth i very light at both high and low bandwidth. Utility i convex at low bandwidth value and tart becoming concave after b min a depicted in Fig. (c). III. A. Network Worth PROBLEM FORMULATION Let u define a new metric: the worth of atified network requet (Network Worth) W k = 2 (i) U k (4) where i i the network priority level. Weight of network requet 2 (i) indicate relative importance of that requet comparing to the other. In thi reearch, it i aumed that there are four priority level, where level i i more important than, for i > ; 4 i,. The priority cheme here i

3 baed on the weighting contant cheme that wa ued in [4]. The goal of thi reearch now i to maximize W. B. Problem tatement Conider a network coniting of a et L of unidirectional link, where a link l L ha capacity c l. The network i hared by a et S of unicat eion ( uer) we aume here all end-uer requet are unicat. Let L L denote the et of link ued by eion S. Alo let S l S denote the et of eion that ue link l L. Each eion ha a minimum required tranmiion rate b min 0, and a maximum required tranmiion rate b max <. For HRT application, we only care for b max, their b min i actually b max. For RT application, both b max and b min have to be defined. A RT flow i only accepted to the network with it allocation lie within [b min, b max ]. For elatic application, their b min by default i 0, therefore only the upper boundary b max hould be defined. Each eion ha a pre-defined utility function U :. Utility function here will be one of the three function defined in Section 2. One thing to notice i that even though two application belong to a ame category (e.g.: elatic application), their utility function are not necearily the ame (becaue of different defined parameter). We define the bandwidth range B = [b min, b max ]. Thu eion ha a utility U (b ) when it i tranmitting at the rate b, where b B. Our obective i to maximize the um of uer atifaction degree (utilitie) over all the eion. The problem can be poed a: max() U b (5) S But a we mentioned before, it will not be fair to treat all requet at the ame weight; therefore we will not try to maximize total network utility, but total worth of network atified requet. Bae on (4) and alo conider the link capacity contraint, we can rewrite the problem a: Subect to max() W b (6) S b cl l L Sl IV. (7) b B S (8) BASMIN Baed on network utility function and problem tatement above, we propoe BASMIN (Bandwidth Allocation Scheme for Multi-ervice IP Network), a bandwidth allocation cheme that aim to the goal of maximizing total worth of all network requet. BASMIN conit of two component: the dynamic bandwidth allocation procedure and the load balancing algorithm. A. Dynamic bandwidth allocation procedure The purpoe of thi component i to accept/reect a pecific network requet and allocate the appropriate network bandwidth once a network requet i accepted. The network edge router maintain one table which record information of all application of all type including the traffic type, conumed bandwidth, it time in the network, it path, it priority level ( i) and it utility function (maximum and minimum bandwidth requeted b max, b min, parameter k, k and k 2 ). When a new connection requet come, the edge router firt claifie thi requet into one of the three predefined categorie: HRT/RT/Elatic tranmiion. Denote the capacity for path p a C p, the bandwidth conumed by all application on path p a R p. The available bandwidth on path p i defined a A p = C p R p. Our heuritic for bandwidth allocation conit of three tep: Step. Admiion control. Thi tep erve a the firt barrier of the network. Admiion control proce will run at network edge router. The miion here i to quickly make a deciion to whether accept/reect a pecific network requet without any afterward information about the network requet. Thi tep i only applied to HRT and RT traffic ince they require a minimum amount of network bandwidth to achieve an acceptable performance. There i no guarantee QoS for elatic traffic, therefore we may bypa their admiion control but thi doe not neceary mean that we accept any elatic requet into the network, they till poibly be reected at Step 2 of thi proce. When a new network connection requet arrive, the new edge router check if there exit a path p with A b. Ye new flow i accepted, go to Step 2. No aume that all exiting application whoe w i new maller that w are preempted and all remaining RT/HRT application take their minimal amount of bandwidth b min, all elatic application take b =, where i the increment ize; then check if there exit a path p again. o Ye new flow i accepted, go to Step 2. o No new flow i reected, procedure terminated. Step 2. Path election. Thi tep i only a calculation tep. We do not really do the bandwidth allocation at thi tep; however, information calculated here i eential for the later tep. Miion aigned to thi tep i to find the bet path to accommodate the new flow, in the cae there are many available path. For each path that can accommodate the new flow, execute the following conequently: Allocate the new flow with the bandwidth amount b min if it i RT, if it i HRT or elatic. p min

4 If there i till available bandwidth, reaccept/increae bandwidth of preempted/degraded flow in the lat tep according to the decreaing order of their worth increment tep: W = 2 (i) U (b) where U (b) i derivative of the utility function at bandwidth b. The proce will be repeated until there i no more bandwidth or all flow reached their b max. Apply the load balancing algorithm (will be dicued later in Section 4.2.) and calculate total worth on each link. Compare the new and old value of total network requet worth for each path W and find the path p with max p network worth increment W then go to Step 3. p Step 3. Bandwidth allocation. Thi i an execution tep baed on reult calculated from Step 2. We will put the new flow into path p and allocate bandwidth among all flow on thi path accordingly to the load balancing algorithm. B. Load Balancing Algorithm Given a path p, all current network traffic flow on p and a new network requet, the goal here i to relocate bandwidth among all traffic flow to maximize the total network worth on thi path. The algorithm contain four tep:. For each flow on path p, allocate it the amount b of min bandwidth, i.e. let b. Calculate R p and A p, if A p = x min 0 or all procee already reach their maximum bandwidth ( x b, max ) the proce i terminated. Otherwie, go to next tep. () i 2. Calculate the potential worth increment w 2 u '() x for each. Find with the larget w. If there i more than one, chooe any one. 3. For flow choen from previou tep, increae it bandwidth by an increment ize, if A p > then go to next tep. If A p, increae the bandwidth of flow by A p. 4. Update A p = A p, return to Step. bmin N/A 2 N/A 3 Mbp 4 N/A 5 N/A 6 N/A TABLE I. bmax 30 Kbp 256 Kbp 4 Mbp 20 Kbp 52 Kbp 5 Mbp TRAFFIC PROFILES IN OUR SIMULATION Data Volume Kbyte 0 00 Utility Func., b , b 0.03, b , b b 2.66 e b network ytem due to it implicity. There i no admiion control or reource reervation here. All traffic flow are accepted to the network and receive a equal hare of the network capacity. However in our experiment, we will improve bet effort a little bit by limiting bandwidth allocated to a flow ( b max ). Thu we improved the cheme effectivene by never over-aigning network bandwidth to a traffic requet. Complete partitioning: Thi i alo a widely ued bandwidth allocation cheme in mid-ize network becaue of it implicity. Bandwidth portion for each application type are arbitrary fixed by network adminitrator. There i no admiion control for incoming network traffic flow, all flow within a ame cla will be allocated the ame equal amount of bandwidth. In thi experiment, we fix the line portion a 0% for HRT, 40% for RT and 50% for elatic traffic. Trunk reervation: Thi cheme wa originally propoed by Ren P. Liu [2]. Admiion control i applied for all traffic clae. An incoming elatic flow i accepted into the network only if the utility level for RT traffic flow at that moment i greater than or equal to ome pre-defined i e e e 4.6b b b 0 4 Example Voice ervice & Audio phone Video-phone & Video conf. Interac. Multimedia & VoD , Paging & Fax Remote Login & Data on Demand File Tranfer & Retrieval Service * N/A = not available / no need to be defined V. IMPLEMENTATION AND EVALUATON In order to evaluate the efficiency of BASMIN, we would like to conduct the experiment of comparing BASMIN with other bandwidth allocation cheme. For thi pecific purpoe in mind, we built a imulator uing Java. Simulation i carried out on the level of flow, with network flow from all traffic clae arie a a Poion proce, and have the duration/ize exponentially ditributed. The traffic i differentiated into the three maor clae with ix repreentative application profile a decribed in Table. BASMIN i implemented along with other three bandwidth allocation cheme for performance comparion purpoe: Bet effort (complete haring): Being the implet cheme; yet Bet effort i very popular and widely ued in many mall Figure 2. Network Simulator in action

5 parameter : If [U rt (b rt (t)) ] then accept the incoming elatic flow, ele reect it; where b i (t) i the bandwidth allocated the traffic flow belonging to traffic cla i at the moment t of the incoming flow. To make a fair comparion between the four bandwidth allocation cheme, we firt conduct an experiment on Total network worth v. Network bandwidth (Fig. 2). Having the ame traffic flow a decribe in Table, we try increae the total bandwidth of network line, thu expecting increment in total network worth. Our propoed cheme BASMIN eaily overthrow Bet effort and Complete partitioning and have a light performance advantage over Trunk reervation a the total network bandwidth increae. The reult trongly proved that our Load balancing algorithm work well in trying to find the bet cenario to allocate network bandwidth and maximizing network uer happine. Fig. 3 i the econd tet for BASMIN, thi time we focu on Average connection worth within a fixed total ytem bandwidth C p = 2 Mbp and an incremented traffic arrival rate. Average connection worth i calculated a: Figure 3. Figure 4. A T dur W [()] b t dt w 0 Tdur Network bandwidth v. Network worth Traffic rate v. Avg. connection worth (9) Figure 5. Traffic arrival rate v. Link utilization level where T dur i the duration of the network eion. Obviouly, A w reflect atifaction degree of each uer in the network, not the total network atifaction degree. It i noteworthy to keep in mind that our algorithm goal i to maximize total network worth W () b, not A w. However, S in the Average connection worth tet, BASMIN till be able to achieve a uperior reult comparing to Bet effort and Complete partitioning. The very nature of Trunk reervation i to accept network connection only if thi connection can bring the network a certain amount of atifaction; therefore it i expectable that they put up the bet reult in thi tet. Thi reult can be explained by the different policie ued BASMIN and Trunk reervation: BASMIN trie to pleae all the uer of the network, while Trunk reervation trie to pleae each uer of the network. Main target for the final tet would be checking link utilization level of each cheme. Mean link utilization i defined a the mean amount of bandwidth being ued on the network. Thi metric will how which cheme i able to ue more of the available bandwidth pace. In fact, it will be pointle to udge the efficiency of a cheme in atifying end uer by looking at their link utilization level. However, thi metric i one of the conventional metric that i widely ued in evaluating network reource allocation policie that why we think it i a good experiment to be conducted. Bet effort with no traffic admiion control mechanim i expected to yield the highet link utilization level amongt the four and the reult in Fig. 4 i quite traightforward. It i alo intereting to look at the performance of Complete partitioning; it ha the wort performance ince the cheme wa unable to adapt to the dynamic network tate. We got a little urprie by the fact that Trunk reervation hold a better performance over BASMIN. In fact, Trunk reervation doe not put any limitation on elatic bandwidth. Including the fact that elatic traffic i dominant in our network traffic profile (3 over 6 traffic profile Table ), Trunk reervation therefore cannot yield the better atifaction degree but have a higher link utilization level comparing to BASMIN.

6 Throughout the three tet, we are very diappointed with reult of Complete partitioning. Complete partitioning i widely ued in many mid-ize network uch a companie, chool, laboratorie with a ingle adminitrator network model. An adminitrator will arbitrary aign a fixed portion of network bandwidth for each application profile and network application will hare an equally amount of network bandwidth a long a they have a ame profile. However, a experimented in thi reearch, thi bandwidth allocation cheme turned out to be not a good idea at all, epecially in the cae of dynamic future network. VI. CONCLUSION The main contribution of thi reearch i BASMIN, a bandwidth allocation cheme that aim to maximize overall network uer happine. In order to experiment the new method efficiency, we alo built a network imulator and compare BASMIN with other three cheme. Throughout the three conducted experiment tet, BASMIN howed olid performance in maximizing uer atifaction degree. Alo note that we defined a new metric (network worth) to meaure uer happine intead of uing the traditional metric (network utility) a other reearche. The evaluation proce alo expoed weakne of one of the mot current widely ued bandwidth allocation cheme. Baed on praieworthy reult o far, we are now thinking about expanding BASMIN with a traffic rerouting mechanim to clean up network traffic and leave more room for incoming network requet. BASMIN and the new traffic rerouting mechanim will then be evaluated in an MPLS network environment. Promiing reult are being achieved and they will be publihed in a near future. ACKNOWLEDGMENT Thi reearch i the output of the Reearch on the Optimal Network Bandwidth Allocation Method for Each Service Cla. The author would like to end pecial gratitude to KREN of MEST for their ponorhip and technical upport during the reearch proce. 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