Load balancing through terminal based dynamic AP ...

Load balancing through terminal based dynamic AP reselection for QoS in IEEE 802.11 networks Nikolaos Papaoulakis, Charalampos Patrikakis, Chryssanthi Stefanoudaki, Platon Sipsas, Athanasios Voulodimos School of Electrical and Computer Engineering National Technical University of Athens, Greece {npapaoul, bpatr, cstef}@telecom.ntua.gr, [email protected], [email protected] Abstract- In this paper, a mechanism for supporting Quality of Service in wireless networks through the dynamic reselection of Access Points (APs) according to a terminal based load balancing scheme is presented. The mechanism is analyzed, and a corresponding implementation on laptops is presented, while evaluation through both simulation and actual field trials are included. Implementation refers to both Linux and MS OS systems. For the evaluation of the mechanism in the field trials, a very demanding streaming video scenario is selected, and the results are assessed with respect to the improvements that can be introduced in the end user Quality of Experience, even over state of the art streaming adaptation solutions. Keywords-load balancing, smooth streaming, AP reselection, WLANs, QoS

I.

INTRODUCTION

The use of IEEE 802.11 Wireless Local Area Networks (WLANs) has been constantly gaining ground over the use of traditional wired LANs in the last years, both in private and public networks. The main reasons behind this are the low cost of implementation in terms of equipment and infrastructure, as well as the flexibility and simplicity offered to end users [1], [2]. Originally, WLANs were used for simple office and home applications as they were destined to provide a wireless extension on the existing wired LANs (Ethernet IEEE 802.3). The main services that they were carrying out were typical IP services, like HTTP browsing, FTP and email. These services have specific Quality of Service (QoS) requirements [3], which WLANs’ well-tested MAC mechanisms like CSMA/CA, RTS/CTS and the Fragmentation mechanisms can easily satisfy. On the other hand, real time services based on multimedia streaming such as VoD, WebTV, and videoconferencing have stricter requirements that increase the significance of key performance indicators such as bandwidth, delay of packet delivery, jitter and error rate [2][3]. The inherent lack of supporting mechanisms for guaranteeing the values of the above indicators in the existing version of 802.11 standards hinders the achievement of enhanced end user experience. Thus, the need for new mechanisms that can introduce the necessary functionality for quality awareness and responsiveness to various network conditions is imperative. In this paper, we present an innovative mechanism that is able to introduce a quality aware feature in already existing WLANs without requiring changes in the protocols utilized

or on the implementation and installation of infrastructure equipment. The mechanism is inspired from similar solutions in the media and content distribution fields [4][5] and tries to address the issue of quality in the offered services through the balancing of traffic load in a similar way that this can happen in a Content Distribution Network (CDN). Here, it should be noted that in contrast to the traditional implementations of CDNs, where central management of the infrastructure is easy to be performed, the ‘ad hoc’ nature of WLANs and the increased mobility of users demands a decentralized approach. Therefore, the proposed solution includes only terminal based experiments and decisions, while it tries to minimize the implementation cost that a commercial deployment of the solution could have. This introduces an extra requirement that the proposed solution should satisfy: the experiments for the discovery and selection of a new AP with better connection and service quality characteristics should affect neither the capability of the terminal as regards networking parameters, nor the quality offered through the existing connection to an AP. The idea of dynamic AP reselection in order to meet the demand for QoS has been proposed in the past through several approaches that focus on the change of the conventional criterion for AP selection in a WLAN, based on the received signal strength of the AP at the client’s device. All these attempts have the same target: to surpass the traditional approach of the “best (strongest) signal” choice and end to the “best quality” choice. The latter combines the classic AP selection procedure with other parameters of the network that define QoS. For this, changes in the 802.11 standard are proposed ([1][2]). The approach proposed here goes beyond the proposals presented so far since it does not introduce changes in the 802.11 standard, nor any hardware and firmware changes in the infrastructure equipment, thus relying on a terminal based, cross- layer solution. The rest of the paper is organized as follows: In Section 2, a reference to the core proposals about supporting QoS in IEEE 802.11 networks is provided. Emphasis is given in AP reselection. Section 3 provides the description of the proposed mechanism, together with details on implementation. Section 4 contains an evaluation based on simulation and actual field trials aimed at assessing the effectiveness of the mechanism. Finally, Section 5 closes the paper with conclusions and discussion on possible uses of the proposed mechanism.

II.

QOS SUPPORT IN IEEE 802.11 NETWORKS

Although IEEE 802.11 is the most widely used WLAN standard today, it does not include any inherent mechanism for QoS support, which is necessary for the increasing number of multimedia applications over IP networks. To overcome this, a large number of 802.11 QoS enhancement schemes have been proposed, each following a different approach, including protocol enhancements, central management schemes and hardware modifications [2]. Following, we will present the most important of those. A. Enhancements to the 802.11 standard: IEEE 802.11k IEEE 802.11k is an amendment to IEEE 802.11-2007 standard for radio resource management. It defines and exposes radio and network information to facilitate the management and maintenance of a mobile WLAN. IEEE 802.11k is the key industry standard that enables seamless BSS transitions in the WLAN environment. The 802.11k standard provides information to discover the best available AP trying to improve the way traffic is distributed within a network. Since in a WLAN each device connects to the AP with the strongest signal, this may lead to excessive demand on one AP and underutilization of others, thus resulting in degradation of overall network performance. Knowledge of AP coverage areas can assist in the provisioning of functionality related to location based handovers or push services. A standard method to get this knowledge is the usage of information obtained by the mobile devices which are currently associated with the APs. The time span required for the discovery of coverage areas and neighbors is fundamental to assess the suitability of such a method in a dynamic environment [5], [6]. B. Supporting QoS at the transport level One important factor as regards the support of QoS of real time streaming in IP networks and more specifically in the case of media services is the technologies used in the transport level. State of the art technologies have overcome the decisions concerning the selection of the appropriate protocol between UDP and TCP and have moved forward to combine the advantages of these two fundamental protocols: the lightweight nature of UDP and the flow and network congestion control mechanisms of TCP. In this way, both efficiency in terms of reduced protocol overhead, but also early congestion notification and confronting mechanisms can be deployed [1]. The Datagram Congestion Control Protocol (DCCP) [7] is the most prominent case; it can address network congestion and flow control problems at the transport level without the excessive overhead of TCP. However, this is a universal solution that is designed to match the needs of IP networks in general, without taking into account the particularities of wireless infrastructures and

is focused on addressing temporal problems in the information transport. Furthermore, it cannot be considered as a comprehensive QoS supporting mechanism. C. QoS enhancement schemes at the MAC level Normally, QoS issues in wired LAN are neglected since the physical layer bandwidth is high enough (1Gbps is now a common link speed between switches in enterprise LANs while 10Gbps 802.3ae Ethernet will appear soon). However, wireless LAN has some distinct features from wired LAN: high bit error rate, high delay and low bandwidth that make high data rate difficult to achieve. Moreover, high collision rate and frequent retransmissions can lead to unpredictable delays and jitters that degrade the quality of real-time voice and video transmission. Currently the two main architectural approaches to add QoS support in the Internet, i.e. Integrated Services (IntServ) and Differentiated Services (DiffServ) are not adequate to address quality problems in WLANs. IntServ presents scalability issues because of its requirement of setting states in all routers along a path, while DiffServ is difficult to map between different service domains or subnetworks such as 802.11 WLAN. D. Using Access Point Reselection in IEEE 802.11 for enhanced QoS In IEEE 802.11 and almost all WLANs, the selection of the serving (dominant) AP from the MS in an infrastructure topology is based on the best coverage offer by an AP, by means of strongest received RF energy level (Rx Level) [1], [5], [6]. In receiving mode, each Mobile Station (MS) has to search for the best dominant AP in its area for a determined Extended Service Set Identification (ESSID). This AP reselection process [5] is based on the comparison of the received Rx level from the AP with the same ESSID that the MS receives. In particular, the formula that describes the AP selection process is: Rx level (new) > Rx level (old) + APRH (1) The parameter APRH (Access Point Reselect Hysteresis) is controlled by the WLAN client and is different for each Wi-Fi vendor. Some vendors provide predefined values for this parameter with the parameter “density of Access Point” such as: High, Medium and Low. Also the use of the Fast Roaming parameter determines how fast a user returns from one AP to another. In particular, this parameter determines the minimum Rx level from the serving AP to enable the mechanism of (1). In IEEE802.11 WLAN networks, distributed Radio Resource Management (RRM) techniques are necessary for the efficient use of scarce radio resources and to perform load balancing to the telecommunication traffic, among all the APs of the infrastructure network. The WiFi load balancing technique is necessary for the efficient use of

scarce radio resources among all the APs of the infrastructure network. In contrast to the IEEE 802.11e framework the proposed technique of the load balancing will provide a cross layer QoS provision in all IP services that could be used also in a multi-vendor and high mobility environment. In these networks, the MS has the functionality to select an AP, based on specific criteria (mainly focusing on the strongest receive signal). A key challenge is how to achieve overall load balancing in the network, during the AP reselection procedure in a way that will achieve the optimum utilization of network resources. In receiving mode, each terminal has to search for the best dominant AP in its area for a determined Extended Service Set Identification (ESSID). This process (called AP reselection) is based on the comparison of the received Rx level from the AP with the same ESSID that the MS receives. Particularly eq. (1) describes the conventional AP selection mechanism. In order to be able to introduce a method for supporting QoS in IEEE 802.11 networks, while keeping the modification requirements to a minimum and without introducing new protocols and infrastructure changes, the only solution is terminal based AP reselection. We now present our mechanism based on this approach. III.

LOAD BALANCING THROUGH TERMINAL BASED DYNAMIC AP RESELECTION

Q WLAN = Rx level (dBm)-f · B (3) B is the traffic indicator and its value ranges from 0 to 5 with 0 indicating that there is no traffic. f is a parameter used as a weight to the traffic indicator and its value ranges from 1 to 4 with 2 the default value. Based on the requirements, the use of any centralized management resource system should not be an option as it would act against compatibility and introduces other technical problems such as the implementation of different hot spots that belong to different service providers or different user-owners. The use of an alternative decentralized and user centric method is presented below, that could address the problem of load balancing, but without the usage of central network management. In this method the calculation of Q WLAN is performed at the mobile terminal on the PHY layer, only requiring a modification on the software driver of the WiFi card. Actually the client card has to get in promiscuous mode and “sniffer” the air interface to determine the resource availability of each AP. In order to present the technique we have to describe some basic IEEE 802.11 features like the beacon packets. Each AP transmits periodically some beacon frames, in order to broadcast administrative information to the MSs. These management frames act as synchronization signals to the network, in order the distributed users to have synchronized clocks with the AP. In Figure 1 the frame and the relevant time periods are depicted [9].

A. Description of the mechanism Minimizing congestion in WLANs comprising more than one AP has received significant attention in the past few years. There are approaches for traffic balancing in WLANs by adjusting the RF power of APs and control the dominance area of an AP and correspondingly the offered traffic according to the traffic load, but these require centralized management and are not easy to deploy in a large scale network, where WiFi APs from several vendors can exist. Instead, an AP reselection mechanism following am approach where the reselection criterion is not limited to the Rx level, but also includes the traffic load of the AP as a parameter selection is more appropriate, especially for large networks spawning over different and heterogeneous domains. For this, an enhancement of the conventional AP reselection mechanism by adding a traffic dependable value, which corresponds to the average throughput utilization, should be included. Thus, the new AP reselection mechanism can be described as: Q WLAN (new AP) > Q WLAN (old AP) + APRH (2) where QWLAN is the parameter measuring the expected quality that can be offered by each AP, measured over the utilization of the AP, over throughput, and APRH (Access Point Reselection Hysteresis) is a parameter used to avoid frequent reselections and its value ranges from 2 to 10 with 4 being the default value.

Figure 1. Beacon frames and time delays [9]

Between the beacon packets each AP could transmit data packets to their clients, by introducing a delay to beacon scheduled transmission that depends on the telecommunication traffic. Every packet that is transmitted from an AP has a sequence number. This number indicates the raw number of the transmitted packets. In order to get a quality measure of the traffic for an AP, we calculate the difference of the sequences numbers of two sequential beacon packets. This difference is scaled from zero to five depending on the AP’s maximum supported rate. One last thing we have to consider is that the traffic sense mechanism should be immunized to self traffic. This is achieved by keeping self traffic packets sequence numbers and taking them into consideration when calculating the traffic load. Using this technique, we can achieve better client distribution to the available APs within an area. In Figure 2, simulation results from the use of WINPROP tool and multi-wall-and-floor model for indoor radio propagation on the dominance areas of 4 APs within an indoor environment are presented. In the upper screenshot we can see the initial user distribution along the 4 APs. In

Figure 2. Network load balancing with modification of dominance areas (upper- without / lower – with the proposed technique)

AP4 (yellow colored dominance area) are positioned 5 users and this AP has increased traffic load when its adjacent AP has less or no traffic. After the application of the proposed technique, the congested AP4 channel2 has a smaller dominance area, which has as a result 3 users to be served now from the adjacent APs. The full description of the implementation and experiments can be found in [1]. B. A terminal based implementation The implementation of the proposed technique has to be based on an additional and enhanced tool for the link-layer network access monitoring. This is very important as there is no need for new firmware from the terminal vendor side and it will be compatible with most network cards. 1) Hardware issues The implementation of the traffic sensing mechanism depends on OS platform as well as on the wireless card driver. To begin with, in order to capture traffic that it is not caused by the wireless adapter, the wireless adapter must be able to run in promiscuous mode. In this mode, however, it cannot be used to transmit and receive data simultaneously. For this, our pilot implementation needs two wireless adapters, οne to be used as a network probe (receiver only) and the other to be used for data exchange. It should be noted that in a commercial implementation, the inclusion of a second receiver in chip, would not increase significantly the cost of WiFi hardware of the portable device. Moreover, since the second receiver will be used just to receive packets, it should not introduce any significant increase in the battery consumption in mobile devices. Figure 3 depicts the end user laptop with the additional WiFi USB card as traffic sniffer (in promiscuous mode). 2) Operating System issues Trying to cover most operating systems, the implementation was tested in both Linux and Microsoft Windows. In Linux systems, there is a library called libpcap which can be used to sniff packets transmitted in wireless networks. With this library we can capture every packet, so both implementations are feasible. When it comes to Microsoft Windows systems things are less simple. For

Figure 3. Laptop with load balance feature

recent Windows OS (Vista, Windows 7, Windows XP SP2) there is an API called Wireless Native API. This API includes functions that control the wireless adapter. Unfortunately, this API does not allow to capture any packets, although, it dispenses most of the information from the beacon packets that the wireless adapter captures. Nevertheless, this information is not enough to implement the traffic sense mechanism. Fortunately, there is one more API, which comes with the program Microsoft Network Monitor. This API allows capturing wireless traffic, so we can combine these two APIs to implement the dynamic AP reselection mechanism. 3) Deployment and integration First, one of the two wireless adapters is configured to be in promiscuous mode and the other is used to connect to the APs and exchange data. The tool, then, enlists all the available APs and analyzes packets for a predefined period of time. Two threads are used by the protocol analyzer, one for self traffic and the other for beacons of all the other enlisted APs. As mentioned above, while sniffing, only beacon packets’ and self traffic packets’ sequence number and signal strength are recorded. When this period of time has elapsed, the tool stops sniffing and calculates the traffic for every enlisted AP. This is achieved using the following steps: The differences between the recorded sequential sequence numbers are calculated, followed by the average sequence number difference and the average signal strength. The average sequence number difference is then scaled from zero to five. This is the B parameter in eq. (3). The scaling is done by taking into consideration the maximum supported speed of the AP. Next, B and the average signal strength are used in eq. (3) and the quality of every AP is determined. When the quality of every AP is calculated, the one with the best quality is chosen. If the second wireless adapter is not connected to any AP then the tool undertakes the task to connect to the AP with the best quality. If the second wireless adapter is already connected to an AP, then eq. (2) is used to determine whether an AP reselection should be performed. The parameters f and APRH have default values, but the user can change their values from the GUI (Figure 7). The tool runs continuously and after the selection of the best AP it restarts the sniffing threads for the next loop.

IV.

EVALUATION - DISCUSSION

A. Simulation based evaluation By utilizing the radio-planning tool WINPROP for indoor WLAN environment [8], we simulate the radio coverage and in particular the best server areas of an infrastructure ΙΕΕΕ 802.11b network with 4 APs. In Figure 2, we use different colors in order to depict the dominance areas of each AP. The black dots represent 5 randomly created users. According to the new AP reselection mechanism, we calculate the Q parameters for every WLAN user and for the adjacent APs 3 and 5. We can see that AP4 has 5 users. Also, we suggest that each MS utilizing IP traffic with Average throughput of 1000 kbps. With the help of WINPROP and multi-wall-and-floor model for indoor radio propagation [10], we know the Rx level received by each user from the corresponding dominant AP. Thus: Quser,AP =Rx level (dBm) + APUtil (4) Within the blue frame we indicate the new dominant AP for each of the 5 users. For the new AP reselection mechanism, we have eq. (2). Finally, we have two users to the APs 3 and 4 and 1 user to AP5, which is a much better distribution instead of 5 users on AP4. B. Lab trials: collaboration with streaming mechanisms In order to evaluate the results of this technique on a real use case scenario, we deployed in the lab a WiFi network based on 3 APs implementing the IEEE 802.11g protocol. Each AP could provide a real IP throughput of 24 Mbps. The trial was carried out by utilizing 4 laptops. Two laptops were running the load balancing application with WiFi USB receivers acting as probes (sniffers) for the air traffic monitoring, while the other two laptops utilizing the conventional AP reselection mechanism of the IEEE 802.11 standard. In one of the laptops, a media streaming application featuring a state of the art mechanism for adapting to network congestion problems based on HTTP adaptive streaming was used. The mechanism was Smooth Streaming [4], an adaptive streaming technique for both Ondemand and Live delivery of contents based on HTTP Progressive Download Streaming, where the video/audio source is encoded at multiple bitrates, and segmented in multiple chunks of video (each with 2-to-4-seconds of video). The technology enables an uninterrupted streaming experience, allowing dynamic adaptation in real-time to the rates of the different streams according to the network and user conditions. The remaining laptops were running FTP applications downloading large files that ensured that the download time would be sufficient to allow us to monitor the performance of the network for an adequate period of several minutes. Figure 4(a) depicts the layout of the trial. As we can see, all laptops were located inside the dominant area of the AP1. The use case scenario consisted of 3 steps.

Figure 4. (a) Lab trial layout (b) Load balanced network

In the first step, the load balancing feature was disabled on the 2 laptops that had the corresponding application installed. The video reception over HTTP was initiated, and after 15 seconds (enough to allow the smooth stream mechanism to reach the optimum possible throughput of 2.4 Mbps) FTP downloads from the rest of laptops (throughput 7.1 Mbps for each of them) was initiated. Figure 5(a) shows the smooth stream feature manage decreasing the stream throughput (until 300 kbps – lower left graph on the figure) in order to maintain the stream but with lower video quality. In the second step, the stream reception was again initiated, but now with the load balancing feature on the media receiving laptop enabled. The same scenario was used for the FTP as well. As we can see in Figure 5(b), in this case we had a small decrease of the streaming throughput and then a stable stream at the initial throughput as the laptop reselects to the AP2 that has better QWlan indicator. This means that the smooth streaming mechanism was not deployed in order to compensate for the reduced bitrate, since this was already handled by the load balancing mechanism. As regards the remaining 3 laptops, these remained connected to the AP1 (throughput 7.1 Mbps). It should be noted that even this short decrease of the smooth stream throughput can be eliminated with appropriate finetuning of the load balancing decision making algorithm. In the third step we enabled the load balancing feature also on the second laptop that downloaded a file over FTP. After a period of 15sec the laptop reselected AP3 i.e. the AP with the best QWlan compared to the other two laptops. Thus we had 2 laptops on AP1 (throughput 11.5 Mbps), 1 laptop on AP2 and 1 on AP3 (24Mbps) (Figure 4(b)). In Figures 6 and 7, we see the switching of the serving AP on the GUI. In Figure 6 we see that the client is locked on the AP with MAC 00:156D:A6:C8:55 with Q = -73 dBm and equal to the Rx level as there is no traffic at the beginning. After the creation of traffic on the serving AP, we have the reselection on the AP with MAC 00:156D:A6:C8:3C with Rx Level=Q=-75dBm as it has better Q value from the previous one with Q=-79 dBm that has a 5dB penalty comparing with the corresponding Rx level (Figure 7). The evaluation of this technique within the results from the trial shows that we could support a much smoother quality using the proposed load balancing method.

Figure 6. GUI and Serving AP without traffic

Figure 5. Smooth stream (a) without and (b) with load balancing.

V.

CONCLUSION

In this paper, we presented a load balancing mechanism for Wireless LANs. The proposed mechanism is terminal based and does not require any modifications to existing WiFi infrastructures or protocols. The only requirement is the deployment of a second wireless adapter module acting merely as a receiver so as to perform quality experiments without affecting the connection bitrate or battery lifetime. Though in our pilot implementation a second adapter was used in order to provide proof of concept that the mechanism can indeed be deployed, in a commercial implementation, inclusion of the second receiver module could be integrated in a wireless access card without significant cost. Since streaming applications constitute one of the most challenging cases for supporting Quality of Service in wireless environments, a related use case scenario was selected for evaluation of the proposed mechanism. The results indicate that our proposal can increase the end user experience even in the case of a state of the art streaming adaptation mechanisms such as smoothstreaming. However, this is not the only case that the proposed mechanism can improve the quality of offered services. All high bandwidth demanding applications and especially time and delay sensitive ones can benefit from the use of the mechanism proposed here. Especially in cases of public access serving large numbers of users, deployment of the mechanism can be used in order to provide distribution of user connection to APs not only based on distance from the APs and signal strength but rather on the actual use of the network. Furthermore, users can benefit from the mechanism in order to avoid the problem of moving around the WiFi coverage area in order to be connected to a different AP in order to get a possibly (but not guaranteed) better service quality. Instead the terminal automatically performs the necessary experiments and dynamically selects the best (and guaranteed in terms of improved service quality) AP. As a future enhancement, we intend to enrich the dynamic reselection mechanism with the necessary features that can incorporate user behavior/profile characteristics, so that these can be used to improve the response of the mechanism.

Figure 7. GUI and Serving AP with traffic

ACKNOWLEDGMENT The research leading to these results has received funding from the European Union's Seventh Framework Programme ([FP7/2007-2013]) under grant agreement n° ICT-215248. REFERENCES [1]

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