ADAPTIVE ADMISSION CONTROL MECHANISM FOR IEEE E WLANS

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1 ADAPTIVE ADMISSION CONTROL MECHANISM FOR IEEE 82.11E WLANS Cristina Cano, Boris Bellalta, Miquel Oliver NETS Research Group - Universitat Pompeu Fabra (UPF) Passeig de la Circumval.lació No. 8, PC: 83 Barcelona, Spain {cristina.cano, boris.bellalta, miquel.oliver}@upf.edu ABSTRACT A novel flow admission control mechanism for infrastructure IEEE 82.11e EDCA-based WLANs is presented. The proposed admission control works jointly with a tuning algorithm which decides the most suitable configuration of the MAC parameters each time a new flow arrives or leaves the system. It is designed to work in heterogeneous scenarios with both elastic and real-time flows, so it aims to: i) guarantee the QoS requirements for real-time flows (like VoIP) and ii) maximize the besteffort throughput (such as TCP web browsing or P2P transfers). Results, compared with the use of the DCF (Distributed Coordination Function) and EDCA (Enhanced Distributed Channel Access) with static parameters, show a clear improvement on the overall HotSpot performance. Our mechanism provides better QoS, uplink versus downlink fairness and a more efficient use of the transmission resources. I. INTRODUCTION Next generation WLAN Access Points (AP) and mobile stations (STAs) will use the WMM (Wireless MultiMedia) specification, which implements a subset of the IEEE 82.11e standard [1], providing static traffic differentiation. Therefore, a natural evolution is: first, to include admission control mechanisms to assure the system stability and secondly, the use of adaptive MAC parameters tuning algorithms in order to efficiently assign the transmission resources under the QoS constraints. These solutions will not change the distributed random access to the channel, which is one of the basic features of the WLAN success [2].. EDCA provides traffic differentiation based on a set of static AC (Access Category) MAC parameters which guarantee a hard and conservative prioritization of real-time flows (with strict bandwidth and delay requirements), reducing the capacity for best-effort flows in heterogeneous traffic environments. However, better performance levels can be achieved for both types of traffic by choosing the MAC parameters of each AC taking into consideration the network state. Then, it is possible to guarantee the required QoS for a major number of rigid flows and, at the same time, maximize the overall best-effort throughput. Although the required signalling messages to support the Admission Control procedure are also defined in the 82.11e standard [1], the admission rules remain still open. There are different proposals to provide QoS in WLANs [3], that are based on the use of admission control mechanisms combined with MAC parameters tuning algorithms [4 6]. However, these are not complete solutions because not all MAC parameters are considered and the admission control is evaluated in scenarios with only one type of traffic (rigid or elastic, heterogeneous scenarios are not studied). Moreover, there are still few works which analyze the flow-level behaviour of the admission control (blocking probabilities and flow transfer delay) [7, 8]. In this work a new admission control mechanism, a MAC parameters tuning algorithm and its performance evaluation in an Hotspot scenario with heterogeneous traffic flows (VoIP calls, P2P and Web flows) are analyzed. The paper is organized as follows: Section II describes the proposed Admission Control mechanism. In Section III the MAC parameters tuning algorithm is presented and in Section IV performance results are provided and analyzed. Finally, Section V concludes the paper and describes ongoing research topics. II. FLOW ADMISSION CONTROL SCHEME The IEEE 82.11e standard [1] specifies the required signalling mechanism to implement an admission control mechanism in EDCA-based WLANs. The admission control applies to any AC when the associated Admission Control Mandatory (ACM) field is activated. In that case, all STAs that want to use this AC must send an ADDTS (Add Traffic Stream) request containing the TSPEC (Traffic SPECification 1 )ofthe new flow. After a reception of an ADDTS request, the Admission Control, based on the received TSPEC and the current system state, decides whether to: i) admit the new flow with the requested TSPEC, ii) suggest another TSPEC that could be admitted by the network, or iii) reject the flow. Once a decision is taken, the Admission Control sends to the corresponding STA an ADDTS response packet, notifying the decision. When the station receives the response, it must decide if it satisfies its traffic requirements. Otherwise, the STA is allowed to schedule another ADDTS request after a period of time. If both the Call Admission Control (CAC) and the STA accept, the flow becomes active. When the flow finishes, the STA must send a DELTS (DELete Traffic Stream) packet to allow the CAC to release the resources used by this flow. Based on the standard signalling protocol we have designed a parameter-based admission control protocol that is depicted in Fig. 1. To decide whether a flow could be accepted, an EDCA analytical model, that is presented in [8], is used to estimate the feasibility of the new state conditions (including the requesting flow). The model allows to evaluate all EDCA enhancements such as the transmission opportunity (TXOP), the use of different inter-frame spaces (AIF S) and different values of the back-off instance (CW min,cw max ). The use of a 1 How a TSPEC is computed is out of the scope of this work /7/$25. c 27 IEEE

2 ADDTS Wait for an ADDTS request or DELTS DELTS Wait for a new flow Flow ends Estimate new state & MAC parameter selection Delete the specified entry in the data-base Compute the requiredtspec Send DELTS request QoS ok? Estimate new state & MAC parameter selection Send ADDTS request Compute the maximum TSPEC allowable for the new flow Send ADDTS response with status SUCCESS and record the new TSPEC in the data base New beacon frame to update the AC parameters SUCCESS Wait for an ADDTS response SUGGEST Is acceptable the suggested TSPEC? Set the new TSPEC Is there a solution? Send ADDTS Response with status RETRY Send ADDTS Response with status SUGGEST RETRY Is the Retry Limit reached? Wait a period of time Retry Limit++ (a) (b) Figure 1: Call Admission Control procedure at (a) AP and (b) STAs detailed model to estimate the next state is required as the channel utilization does not increase linearly with the bandwidth requested by the incoming flow. This is caused because there is a bandwidth penalty due to the distributed channel access that depends on the incoming flow characteristics. Thus, each time a new flow arrives or leaves the system, the CAC decides i) if the new flow could be accepted and ii) the most suitable configuration of MAC parameters. In Fig. 1a a scheme of the entire process at the Access Point is depicted while in Fig. 1b the procedure the STAs have to follow is shown. III. AN HEURISTIC MAC PARAMETERS SELECTION ALGORITHM An heuristic real-time algorithm to select the most suitable combination of the MAC parameters is presented in this section. The algorithm is based on the results presented in [9], where the optimal tendencies of the MAC parameters were studied. Moreover, in [9] an iterative algorithm is also presented. The development of real-time algorithms is crucial to provide practical solutions that could be implemented in commercial APs and wireless network cards. Moreover, they have to use only the information that the Admission Control can store, such as the number of active flows at both uplink and downlink (n DL, n UL ), the bandwidth requested by each rigid flow (B r ) or, in case of an elastic flow, the minimum bandwidth required (B e,min ). The algorithm is based on a well-known performance result of the DCF, which states that the maximum load that a WLAN is able to carry is below the 8% [6]. However, this assumption is not general as depends on the traffic profile of each flow and it does not hold for VoIP calls as the AP saturates at lower loads. Therefore, the algorithm is designed to be general and as a particular case, here it is evaluated with VoIP and elastic traffic. The algorithm (Algorithm 1) starts computing the minimum aggregated bandwidth, β, required by all flows (including the flow which is requesting service). Using this value and the maximum physical bandwidth achievable, the data rate R data (a single data rate is considered for the AP and all STAs), the MAC parameters are adjusted based on a set of predefined thresholds. As can be seen only best-effort (BE) and voice (VO) access categories have been considered. Note that in the computation of β, and assuming that the best-effort flows have a very low minimum bandwidth, the β value reflects the load of real-time traffic. When β is low, the best-effort MAC parameters (AC BE) make possible that best-effort flows could achieve a high throughput performance (with high TXOP values and lower AIFS). However, as the load of sensitive flows increases, the AC BE parameters must reduce the throughput of best-effort flows. For the streaming MAC parameters (AC VO) only the TXOP is adjusted, which increases with β. Theα parameter is set to 9 (it gives the gradient (strength) of the parameter changes) and γ is set to.4 (as it assumed that only the 4 % of the R data capacity is effectively used in uplink / downlink WLAN networks). The value of both parameters are derived from the outcomes of the iterative algorithm [9]. The highest threshold is set conservatively to 7% (note that

3 Table 1: Values of MAC parameters Parameter Value Parameter Value CW min,be,ul [32-124] CW min,be,dl [32-124] CW min,v O,UL 8 CW min,v O,DL 8 CW max,be,ul 124 CW max,be,dl 124 CW max,v O,UL 32 CW max,v O,DL 32 AIF SN BE,UL [3-1] AIF SN BE,DL [2-9] AIF SN VO,UL 2 AIF SN VO,DL 1 TXOP UL [1-1] TXOP DL [1-1] it is lower than the previous 8%) ofther data, assuming that it is the maximum efficiency that the Hotspot can achieve. The other thresholds are uniformly distributed between the interval from 35% to 7% in order to reduce progressively the besteffort traffic contention. The values of the fixed MAC parameters and the ranges of the dynamic ones are shown in Table 1. The problem of the downlink unfairness in WLANs using the DCF random access protocol has been intensively studied as one of the limitations of this type of networks [1, 11]. It appears because the AP and the STAs have the same probability to access the channel as the MAC protocol is designed to provide a node-based fairness (two nodes with a packet ready to be transmitted have the same probability to access the channel). However, the AP usually carries more traffic than any other node in the cell. For instance, in a symmetrical-flow scenario the AP carries the same amount of data than the rest of STAs requiring to access the channel more often. To mitigate the uplink versus downlink unfairness, the proposed algorithm selects different MAC parameters for the AP and for the STAs (Algorithm 1). For each AC theaifsof the downlink will be one unit lower than the one used for the STAs and the TXOP is increased proportionally as the number of flows in the downlink (n BE/V O,DL ). Finally, the CW min of elastic uplink flows is also set depending on the number of active STAs contending for the channel with the AP (n BE,UL ). Once the MAC parameters are selected, the admission control estimates whether the new state is feasible (all flows achieve their required QoS). Notice that a single estimation step is done, reducing the high computational cost of using an iterative algorithm, which requires to estimate at each step if the selected MAC parameters are suitable to accept the new incoming flow. However, there is a performance cost that will be evaluated in the following section. IV. PERFORMANCE RESULTS A flow-level simulator has been developed using the COST (Component Oriented Simulation Toolkit) simulation engine [12]. The simulator models the behaviour of the flow admission control, the MAC parameters tuning algorithm and the behaviour of the STAs (flow arrival and departure). The packetlevel behaviour is modeled using the EDCA analytical model presented in [8]. For the sake of simplicity we have not considered the suggest possibility depicted in Fig. 1. Algorithm 1 Real Time MAC Parameters Tuning Algorithm 1: β = n DL/UL,BE B e,min + n DL/UL,V O B r 2: STAs AC Parameters: TXOP BE,UL max(1, 1 α β γ R data ) TXOP VO,UL min(1, 12 TXOP BE,UL ) AIF SN BE,UL min(1, 3+ α β γ R data ) Set the CW min,be,ul to: 124 if (β >.7 R data ) 512 if (β >.5 R data ) 256 if (β >.45 R data ) 128 if (β >.4 R data ) 64 if (β >.35 R data ) 32 if (β <.35 R data ) if(n BE/V O,DL > ) CW min,be,ul min(124, 32 n BE,UL ) 3: AP AC parameters: AIF SN BE,DL max(1,aifsn BE,UL 1) TXOP BE,DL min(1,txop BE,UL n BE,DL ) TXOP VO,DL min(1,txop VO,UL n VO,DL ) CW min,be,dl CW min,be,ul A WLAN Hotspot with a single AP providing service to a group of STAs is simulated. Each STA carries a single traffic flow, which can be generated from a VoIP, P2P or Web application (see Table 2 for the flow characteristics of each type). All flow arrival rates follow a Poisson process and all flow durations and lengths are exponentially distributed. However, the P2P and Web flow lengths depend on the bandwidth assigned, which changes dynamically with time. TheSTAsand theapuse the EDCA operation mode and the DSSS PHY specifications in the 2.4 GHz band [2]. Ideal channel conditions are assumed, i.e., no packet is lost due to propagation impairments or hidden/exposed terminal problems. The system parameters are reported in Table 3. This scenario is based on the E-MORANS (Extended Heterogeneous Mobile Radio Access Networks Reference Scenarios) WLAN single cell reference scenario [13]. The number of nodes with VoIP (bi-directional rigid flows), downlink P2P and Web flows are fixed to n VO = 4, n P 2P,DL =2and n WEB,DL =2respectively, while the number of nodes with P2P uplink flows ranges from to 25. In[8] it is shown that the uplink best-effort flows cause the worst performance degradation in rigid flows (especially to the downlink ones). Therefore, it is expected that both EDCA and the enhanced EDCA algorithms are able to solve this rapid performance degradation of the rigid flows. In all cases, the admission control is used, so the system never works under saturated conditions. In Fig. 2 the VoIP blocking probability is shown. Note that the blocking probability of VoIP flows using EDCA-Iter (the iterative algorithm to tune the MAC parameters [9]) and the realtime algorithm (EDCA-RT) are lower than the one obtained by EDCA. Obviously, in all this cases, there is a high gain compared with the regular DCF. To asses this improvement, both EDCA-Iter and EDCA-RT decrease the instantaneous elastic

4 Table 2: Traffic Flow Characteristics Characteristic Web P2P VoIP Application/Codec Web browsing File sharing G.711 Type Elastic Elastic Rigid Bandwidth > 1 Kbps > 1 Kbps 64 Kbps Packet Length 15 bytes 15 bytes 2 bytes Average flow duration/total transfer 13 Kbytes 1 Mbytes 24 s Average Inter-arrival Time 3 s 1 s 6 s Table 3: System parameters of the IEEE 82.11b specification [2] Parameter Value Parameter Value R data 2 Mbps R basic 1 Mbps AIFS:A j from Algorithm 1 R PHY 1 Mbps SIFS 1 µs CW min from Algorithm 1 TXOP (msec) from Algorithm 1 CW max from Algorithm 1 SLOT (σ) 2 µs ACK 112 R basic RTS 16 R basic CTS 112 R basic MAC header 24 R data MAC FCS 32 R data PLCP preamble 144 R PHY PLCP header 48 R PHY Retry Limit (R) 7 K (Queue length) 2 packets throughput by adjusting the affordable bandwidth of best-effort flows by tuning the AC BE and AC VO MAC parameters. Notice also that the results obtained with the real-time algorithm, EDCA-RT, are really close to the ones obtained by the iterative algorithm. Moreover, this shows the ability of the proposed tuning algorithm to reduce the impact of the elastic traffic over the VoIP calls. Observe that without elastic traffic, the VoIP blocking probability is zero (as the maximum number of simultaneous calls is lower than the system capacity) and this value remains equal and independent of the P2P Uplink flows when EDCA-Iter is used, and it is very low for the EDCA-RT algorithm. The blocking probability for elastic flows (including both P2P and Web flows) is shown in Fig. 3. Note the clear correlation between the regions where the VoIP blocking probability increases and the peaks in the elastic blocking probability. For instance, in case of using the DCF, when all incoming VoIP calls are rejected (there are no active calls in the system), there is not any reason to block elastic flows, as the minimum bandwidth requirement (1 Kbps) is always achieved, so the blocking probability for best-effort flows decreases to values near zero. The same result is observed when EDCA is used, it increases with the load but it begins to decrease as the blocking probability of streaming flows increases. However, the unfairness between the uplink and downlink combined with the starvation of elastic flows at the AP increase the blocking probability at the downlink if compared with the one in the uplink. Using DCF and EDCA an unfairness throughput behaviour between the AP and the STAs is observed in Fig. 4. It can be seen how the EDCA-Iter and EDCA-RT algorithms mitigate this effect, by increasing the downlink throughput. However, they also provide an overall higher Hotspot elastic throughput VoIP Blocking Probability DCF EDCA EDCA Iter EDCA RT Figure 2: Blocking Probability of VoIP calls than using EDCA. Results clearly show that the overall cell performance is increased and a better use of the transmission resources is obtained: i) the requirements of sensitive traffic are significantly improved and ii) the average best-effort traffic is increased (compared with EDCA) in a wide range of possible situations. V. CONCLUSIONS AND FUTURE WORK This paper studies the problems related with the QoS assurances in IEEE EDCA-based networks. We identify Call

5 Elastic Blocking Probability UL DCF UL EDCA UL EDCA Iter UL EDCA RT DL DCF DL EDCA DL EDCA Iter DL EDCA RT Average Elastic Throughput (bps) 15 x 15 1 UL DCF UL EDCA 5 UL EDCA Iter UL EDCA RT DL DCF DL EDCA DL EDCA Iter DL EDCA RT Figure 3: Blocking Probability of Elastic Flows Figure 4: Average bandwidth used by Elastic flows. Admission Control and an effective tuning of MAC parameters as basic procedures to improve the performance of WLAN networks. The results presented show how our proposal is able to improve impressively the Hotspot performance: i) showing lower blocking probabilities for both real-time and elastic traffic, ii) increasing the best-effort throughput if compared with EDCA and DCF and iii) increasing the fairness between the uplink and downlink flows. The simplicity of the EDCA-RT mechanism make it able to work in real-time in common IEEE 82.11e compliant Access Points and network cards but it also maintains the near optimal MAC parameters configuration an iterative algorithm could achieve. This study can be further expanded taking into account the impact of node mobility, the consideration of multiple transmission rates and non-ideal channel conditions in order to design admission control procedures accordingly. REFERENCES [1] IEEE Std 82.11e. Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications; Amendment: Medium Access Control(MAC) Quality of Service Enhancements. IEEE Std 82.11e, 25. [2] IEEE Std Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. ANSI/IEEE Std 82.11, 1999 Edition (Revised 23). [3] Qiang Ni. Performance Analysis and Enhancements for IEEE 82.11e Wireless Networks. IEEE Network, July/August 25. [4] Albert Banchs, Xavier Pérez-Costa, and Daji Qiao. Providing Throughput Guarantees in IEEE 82.11e Wireless LANs. ITC Specialist on Providing QoS in Heterogeneous Environments Seminar, Berlin, Germany, September 23. [5] Dennis Pong and Tim Moors. Call Admission Control for IEEE Contention Acess Mechanism. IEEE Globecom 23, San Francisco, USA, December 23. [6] A. Ksentini, A. Guéroui, and M. Naimi. Adaptive Transmission Opportunity with Admission Control for IEEE 82.11e Networks. ACM/IEEE MSWIM 25, Montreal, Quebec, Canada, October 25. [7] Deyun Gao and Jianfei Cai. Admission Control in IEEE 82.11e Wireless LANs. IEEE Network, July/August 25. [8] B. Bellalta, M. Meo, and M. Oliver. VoIP Call Admission Control in WLANs in Presence of Elastic Traffic. IEEE Journal of Communications Software and Systems, January 27. [9] B. Bellalta, C. Cano, M. Oliver, and M. Meo. Modeling the IEEE 82.11e EDCA for MAC parameter optimization. Het-Nets 6, IlKley, UK, September 26. [1] C. Casetti and C.-F. Chiasserini. Improving Fairness and Throughput for Voice Traffic in 82.11e EDCA. IEEE PIMRC 24, Barcelona, Spain, September 24. [11] Jiwoong Jeong, Sunghyun Choi, and Chong kwon Kim. Achieving Weighted Fairness between Uplink and Downlink in IEEE DCF-Based WLANs. IEEE QoS in Heterogeneous Wired/Wireless Networks (QShine 5), Orlando, FL, (USA), August 25. [12] Gilbert (Gang) Chen. Component Oriented Simulation Toolkit. cheng3/, 24. [13] NewCOM AP-7 partners. NEWCOM single-cell WLAN reference scenario. NewCOM NoE AP-7 Last Deliverable, January 27.