Autonomic Interface Selection for Mobile Wireless Users - CiteSeerX

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Autonomic Interface Selection for Mobile Wireless Users Claudio Casetti Member, IEEE, Carla-Fabiana Chiasserini Member, IEEE, Roberta Fracchia Member, IEEE, Michela Meo Member, IEEE

Abstract—We address the problem of self-configuration of user stations in a wireless environment with overlapping coverages. We propose and investigate a solution operating on top of the transport layer, called AISLE (Autonomic Interface SeLEction), that exploits nodes featuring multihoming capabilities, i.e., with more than one network interface. Our solution is independent of the technology used at the physical and MAC layers. To evaluate the performance of AISLE, we first analytically derive the optimal way in which mobile stations should partition across multiple overlapping wireless networks, and we then verify through simulation the AISLE ability to achieve the optimal station partitioning. Different scenarios are considered, that include the case of heterogeneous networks, such as 802.11 WLANs and 3G cellular networks, with stations moving across an area with different degrees of overlapping coverages, as well as the case of heterogeneous stations, in which only some stations adopt AISLE.

I. I NTRODUCTION Adjectives like “pervasive”, “ubiquitous”, “self-configuring” are today invariably associated to terms such as “Internet” or “computing”. It is often envisioned that tomorrow’s networks will be so deeply rooted in the surrounding environment that hardly any activity can be undertaken without interacting with some wireless technology. Futuristic though it may sound, our daily experience tells us that we are not very far from being constantly exposed to a network, either because our laptop associates to a WLAN, our cell phone connects to the Internet through a 3G network or it “accidentally” establishes a Bluetooth link to a neighboring appliance. However, this picture is not complete. While we are indeed more than likely to be within the coverage of some network, we are still nowhere near being able to exploit it to achieve uninterrupted, seamless connections when on the move. Besides, it would be highly desirable that the kind of pervasive Internet that is being envisioned be more automated, self-configuring, adapting to changing environmental conditions, and less reliant on human intervention. In a word, that it be “autonomic” [1]. By way of example, a popular wireless technology, such as IEEE 802.11 WLANs, offers a service that is neither seamless nor autonomic. Public areas covered by several Access Points (APs) do not give users freedom of movement without forcing them to associate to new APs as they change positions. The very process of associating to an AP is normally the result of signal strength measurements, and does not provide users with any indication as to the level of congestion (and, thus, C.Casetti, C.-F. Chiasserini and M. Meo are with the Dipartimento di Elettronica, Politecnico di Torino, Italy. R. Fracchia is with Motorola Labs. in Paris, France.

the minimal level of QoS they can expect) on the AP they are about to associate with. A typical situation comes from the laying out of overlapping IEEE 802.11b and 802.11a coverages over the same area in order to fill the connectivity gaps and enhance the network capacity. However, there is no way for users to know whether they are all flocking to the same AP and being overwhelmed by congestion. Similarly, if the same area features two different technologies, such as WLAN and 3G cellular networks, users have no means to know when it is advisable to leave a crowded AP and establish a 3G connection (if both charge comparable fees). The purpose of this work is to devise a solution for multiinterface mobile nodes to efficiently, and autonomically, select the Point of Attachment (PoA) to the network that provides the best performance among two or more overlapping ones. Our solution, named AISLE (Autonomic Interface SeLEction), is implemented on top of the transport layer, thus making it independent of MAC and physical layer technologies. The only requirement is that the transport protocol can efficiently handle multiple interfaces. One such protocol is SCTP [2]. While the choice of implementing the solution at the transport layer requires that careful mechanisms are designed to distinguish losses due to channel conditions from losses due to congestion, it has the advantage that the real quality perceived by the user in terms of connection throughput can be accounted for and optimal fairness at the transport layer can be provided. AISLE determines the wireless interface to be used for data transfer while a node is on the move, by constantly monitoring the available bandwidth and the capacity between transport-layer endpoints, over both primary and secondary paths. In order to evaluate the performance of AISLE, we analytically derive an optimal operation point that corresponds to the stations partitioning among overlapping networks in such a way that all stations achieve the same throughput and this common value of throughput is maximized, within the constraints of bandwidth availability. By simulation results, we will verify the ability of AISLE to reach such optimal partitioning and we will show that, as a byproduct of the strategy, the distribution of nodes across neighboring PoAs is such that the load is evenly balanced and, given that AISLE stations receive the same throughput, the node’s throughput is maximized. The remainder of the paper is organized as follows. An overview of the AISLE mechanism and of the network scenario under study is given in Section II. Section III briefly reviews some related papers and describes the main features of the legacy SCTP protocol. In Section IV, details are provided

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node S #1

node S #1

node S #2

node S #2

fixed network

fixed network PoA #3

path #2

path #1

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PoA #2

PoA #1 3G NodeB

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fixed stations

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Fig. 1. Network scenario: static configuration (left) with overlapping coverage by neighboring PoAs; dynamic configuration (right) with two disjoint cells combined with an umbrella cell

and N wireless stations, each equipped with one or more radio interfaces. The left part of Figure 1 illustrates such a scenario with K = 2, namely, PoA1 and PoA2 , as will be in most of our simulation scenarios. No ad hoc connectivity is considered, however multi-interface stations can connect simultaneously to different PoAs. At the transport layer, stations establish connections with fixed nodes Si , which are linked to the PoAs through the Internet. Single-interface stations do not exploit multihoming; multi-interface stations, instead, can choose from up to K available paths, corresponding to the wireless connections with the PoAs, and run the AISLE mechanism on top of SCTP. We are interested in the case where the PoAs operate under identical or different technologies. Specifically, when deriving performance results, we assume that PoAs use either the IEEE 802.11a/b or the UMTS standard, although our approach is general enough to be extended to other technologies as well. The station transmission rates depend on the employed technology. By allowing stations to optimally select the radio interface to use for data transfer, AISLE leads to an optimal distribution of users between the PoAs. In particular, we will show that, when AISLE is employed, the system closely approaches the optimal behavior, maximizing the total bandwidth usage, while sharing the available resources in a fair manner. Such an optimal behavior is attained also in complex situations, with single-interface stations and data rates depending on the distance from the PoAs or on the channel quality. The last important remark is that AISLE hinges on the assumption that stations are engaged in bulk traffic transfer. We highlight that this does not alter the validity of our study since (1) stations that perform occasional, short-lived data transfers only have a transient impact on the system and (2) AISLE can be easily extended to support streaming multimedia traffic by incorporating the features of Partial Reliability SCTP [4].

on the estimation and selection process that allows AISLE to determine the most performing interface, while in Section V we derive the optimal, fair operating point that will be used as a reference to assess AISLE performance. Results of ns2 simulations are presented in Sections VI–VIII; finally, Section IX summarizes the findings of this work. II. T HE AUTONOMIC I NTERFACE S ELECTION M ECHANISM : OVERVIEW We envision mobile nodes equipped with multiple radio interfaces, and we aim at designing an autonomic mechanism that enables nodes to optimally select the radio interface to use for data transfer. Furthermore, we would like the autonomic scheme to operate independently of the technology employed at the physical and MAC layers. The mechanism we propose, named AISLE, makes decisions based only on what a wireless node perceives from the network environment. AISLE operates on top of a multihoming transport protocol, such as SCTP, exploiting the fact that multi-interface nodes can handle multiple end-to-end paths. Out of the existing paths, one is chosen as primary and used to transmit new data packets while the others are considered as secondary and used for data retransmissions. According to AISLE, a multi-interface node estimates the available bandwidth on the existing paths by using techniques that were first proposed in TCP Westwood [3]. Based on such estimates, the node selects as primary the path that provides the highest available bandwidth. Note that establishing different paths corresponds to using different IP addresses at the connection end-point, and different IP addresses identify different interfaces. Hence, by choosing the primary path, a node determines the radio interface to employ for data transfer. In particular, AISLE selects the radio interface that maximizes the node throughput, and, thanks to accurate bandwidth estimates on the existing paths, it dynamically adapts to the changes in the network environment, such as throughput decrease because of newly established connections. For the sake of concreteness, in the following we focus on a wired-cum-wireless network scenario. We consider K PoAs

III. BACKGROUND A. Related Work Several papers, recently featured in the literature, deal with issues related to our study. Some of these works provide per2

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formance evaluation of SCTP in wireless networks; others try to exploit SCTP features to improve throughput and reliability over multiple paths, which is one of our goals as well. Regarding SCTP performance evaluation in wireless networks, [5], [6] (just to name a few) find that, using the multihoming approach, SCTP can provide higher throughput and robustness with respect to TCP. New, improved versions of SCTP have been proposed in several multihoming/multipath scenarios. A family of such new proposals aims at splitting the load of a connection between the available paths possibly involving different access technologies. Initially studied in [7] in the framework of standard SCTP, load splitting is further investigated in [8], where path monitoring through loss rate and delay is used to replace the simplistic timeout-based selection used in standard SCTP. In [9], SCTP has been modified to accomplish data striping thus exploiting the protocol native support for the simultaneous use of multiple interfaces. Similar in spirit to our work, a few SCTP versions have been proposed to select the most convenient path, possibly involving different access technologies. An interesting approach can be found in [10], which is based on packet-pair bandwidth estimation and Round Trip Time (RTT) measurements. In [11] a modification of SCTP is proposed to support mobility at the transport layer in a general context, while in [12] and [13] multihoming is applied within a UMTS/WLAN overlay architecture to improve throughput performance. Interface selection algorithms for multihomed mobile hosts, also relying on SCTP, can be found in [14] and [15]. Other authors have considered schemes, not involving SCTP, for the roaming across heterogeneous access networks. Some of these proposals are based on the introduction of new elements in the network architecture: in [16] new MAC and PHY sensing techniques are used to accurately derive network conditions and a local connection translation is introduced to make mobility transparent to applications. In [17] TCP is modified in order to recognize impeding handoff and try to adjust the data rate after the handoff. Different works focus on the selection of a point of access when more than one is available, with the aim of being “Always Best Connected” [18]. In [19], the 802.11 AP selection is performed by evaluating the available bandwidth at the MAC layer: the authors propose a methodology to derive the available bandwidth from the delay of Beacon frames sent by the APs. In [20], a middleware system is presented which detects the available AP and selects the best connection in terms of cost and QoS (Radio Signal Strength measured at the physical layer and RTT). Note that, unlike AISLE, these solutions are based on measurements performed at the physical and MAC layers. As previously mentioned, the core idea of AISLE is that, by being implemented at the transport layer, it allows the provision of fairness in terms of connection throughput, as perceived by the end user. The work in [21] shows, through both analysis and simulation, that the DCF access scheme of 802.11 forces the non-cooperative nodes to use inefficient transmission strategies, thus reducing the network throughput. The fluid models proposed in [22] describe the behavior of multihoming users that split their traffic among all the available APs, based on the throughput they obtain and

the charged price. There, it is shown that, under the considered pricing scheme, the total system throughput is maximized. In [23], the authors propose a control of the frequency and duration of users associations in case of network congestion, maintaining a queue of users requesting the network access. Users in the queue are accommodated until the network is completely loaded, while the others wait in the queue for admission. Finally, the AISLE solution proposed in this paper draws on our previous work [24] and explores new directions. In [24], the main objective is to improve the transport layer performance of wireless nodes using the SCTP protocol. In particular, we aim at effectively distinguishing losses due to congestion from those due to the wireless channel when selecting the best end-to-end path. Here, instead, our goal is to provide a mechanism for autonomic networks that optimizes the radio interface selection.

B. The legacy SCTP protocol SCTP has been developed by the IETF Signaling Transport (IETF SIGTRAN) workgroup, and it has been approved as a Proposed IETF Standard [2]. Like TCP, SCTP offers a pointto-point, connection oriented and reliable delivery service. It also inherits TCP’s congestion control scheme, hence it is TCP-friendly by definition. SCTP was designed to overcome many TCP limitations, exhibiting several innovative features such as: • • • •

unordered packet delivery, like UDP; multistreaming with independent delivery of data streams; selective acknowledgment; multihoming.

Despite the relevance of SACKs, also implemented in recent versions of TCP, the main novelty of SCTP is multihoming: SCTP stations with multiple interfaces can establish an association between them, each identified by a separate IP address. If one of these addresses fails, the destination can be reached through an alternate interface. This feature is especially useful in wireless networks where different interfaces correspond to different radio channels, or even different access technologies. SCTP uses multihoming only for redundancy: every station chooses a primary destination address, normally used for the transmission of new packets (called chunks in SCTP lingo), whereas the alternate addresses are considered as secondary, or backup, paths, whose conditions are periodically monitored through the transmission of probe packets called Heartbeats. In standard SCTP, the backup paths are used only (i) to retransmit lost chunks, in order to increase the probability of successful retransmissions; (ii) to transmit new chunks when, due to the expiration of several (namely, five) consecutive timeouts on the primary path, the primary interface is declared as “inactive”. In the latter case, SCTP transmits new chunks toward a backup destination address and Heartbeat packets toward the primary one. As soon as the Heartbeats reception on the primary interface is confirmed, its state is toggled to “active” and the transmission over the primary path is resumed. 3

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IV. T HE AISLE

measuring the separation between back-to-back packets. The basic idea is described in [25]: if two packets are queued next to each other at the bottleneck link, then the resulting interpacket spacing after the bottleneck will be proportional to the bottleneck capacity. Under ideal conditions, if P is the packet size and ∆t is the separation of packets after the bottleneck, the bottleneck capacity is given by

MECHANISM IN DETAIL

We now proceed to clarify the relationship between SCTP and AISLE, describing the details of the path selection algorithm implemented by the latter. A. Building AISLE on top of SCTP In AISLE, we leverage SCTP multihoming and extend its use beyond the case of interface failure: an alternate interface is chosen also in case of degraded performance of the currently active interface. AISLE is therefore fashioned as a new protocol, specifically aimed at wireless networks and based on SCTP. AISLE has two main objectives, which enhance the service model offered by standard SCTP: (i) providing a transparent mechanism to perform association in a multi-interface environment; (ii) efficiently using multiple destination addresses of a single end-point to improve its throughput performance. Efficient use of multihoming is AISLE’s real trump. By efficient it is not only meant that a single AISLE station aims at maximizing its performance in terms of throughput, latency or packet losses. As will be spelled out below, AISLE is designed so that the overall performance of the networks to which AISLE stations are attached benefits from its multihoming capability. Essential to the objectives just outlined is the estimation of the available bandwidth and of the capacity on each of the paths toward the destination addresses. Firstly, AISLE is required to evaluate the amount of available bandwidth on the primary path to efficiently select the radio interface to use; this also allows the identification of losses due to congestion from those caused by errors on the wireless channel. In particular, AISLE inherits the congestion control designed for WiSE [24], whose main aim is to properly set the congestion window (cwnd) size according to the different kinds of losses. Secondly, AISLE is required to evaluate the capacity of all paths in the association; this knowledge, possibly coupled with the available bandwidth estimation, is used to enforce a dynamic redefinition of the identity of primary and secondary paths. In the following, we elaborate further on the two main objectives of AISLE, starting from the bandwidth/capacity estimation process.

P . (1) ∆t Our approach to capacity estimation is based on this technique; in particular we rely on the Packet Pair method proposed in [26] that represents a good trade-off between estimation accuracy and simplicity. More specifically, AISLE replaces the SCTP transmission of the two Heartbeat packets (usually every 15 seconds) with the transmission of a 6-packet train on all active paths (2 small - 2 large - 2 small), like the SProbe Tool [27]. The small packets are 40-bytes-long, like standard SCTP Heartbeats; the large ones have a size of 1500 bytes, a common MTU size. We estimate the bottleneck capacity from the dispersion of large packets that, requiring longer processing time at nodes, have higher probability of being queued. The measure is valid only if there are no losses and packets are received in order. If the measure is valid, the value of the dispersion ∆t is returned to the sender by the Heartbeat ACK (e.g., using a timestamp field1 ) and it is stored as the capacity of that path. Finally, the different packet size provides another heuristic test of the estimate validity: if no cross-traffic is spacing the probe packets and wireless errors do not cause too many retransmissions at the MAC level, the inter-arrival time between big packets should be longer than the one between small packets, otherwise the measurement is discarded and the previously stored value is considered as capacity estimation of the path associated to that interface. Although the 6-packet train generates a small additional overhead compared to Heartbeat packets, they are essential for a non-trivial bottleneck capacity estimation. As a tradeoff, the additional overhead allows AISLE to better exploit the available capacity on all paths and to offload otherwise congested paths. As will be explained below, our path management algorithm requires the capacity estimate on both primary and secondary paths. Therefore, unlike SCTP, AISLE provides for probing Heartbeats transmission on the primary path as well. Also, we remark that, although Packet Pair methods are often considered too simplistic, the technique described above turned out to be very effective for the capacity estimation over the available paths. Packet-pair probing techniques are ineffective as far as bandwidth estimation is concerned. Other solutions have been successfully introduced, such as ACK information filtering used by TCP Westwood [3]: the return of a TCP ACK to the sender heralds the reception of an amount of data at the receiver in a time interval equal to the time elapsed since the reception of the previous ACK. Low-pass filtering of such information has been shown to provide a close estimate of the Cb =

B. Available bandwidth and capacity estimation Throughout this work we will use the term “capacity” of a link l to define the highest possible bit rate at which data are transmitted on link l. This quantity will be identified as Cl . Also, we will refer to the capacity of a path as the capacity of its bottleneck link. If link l carries a time-varying traffic load Rl (t), then the “available bandwidth” on that link will be defined as Bl (t) = Cl − Rl (t). The available bandwidth of a path is also bounded by the minimum available bandwidth across all of its links. Capacity estimation and available bandwidth estimation are traditionally carried out in different fashions. The Packet Pair technique [25], [26] yields estimates of the path capacity by

1 In this paper, we do not address implementation issues regarding the modified format of Heartbeat and Heartbeat ACKs.

4

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available bandwidth on the path between sender and receiver. Since bottlenecks on the return path can determine ACK compression, and consequent sharp bandwidth surges in the estimate (overestimation), a slightly modified procedure has been suggested in TCP Westwood+ [28]. Bandwidth samples are collected, but they are filtered only once every RTT, thus avoiding that the denominator (time between back-to-back ACKs) in the bandwidth sample is too small because of the ACK compression. One drawback of Westwood estimation is that it requires a continuous stream of data packets and of ACKs: in our case, it can only be applied to the primary path. In AISLE, we measure the inter-arrival time between two consecutive SACKs and the amount of data confirmed by SACKs. Then, a Westwood+ filtering is used to derive an available bandwidth estimate on the primary path. As a final remark, a more detailed investigation of the effectiveness of Westwood estimation can be found in [24], in which we showed the accuracy of the adopted technique.

control mechanism); 2) the path available bandwidth is smaller than 10% of its capacity. The latter condition allows the triggering of path selection also for stations that do not experience losses because their transmission rate is limited by a very small congestion window. Supposing that an AISLE station has connections to K PoAs, and thus has K paths available to its destination, consider that path p is Pthe primary path at a given time. Let us denote by πj = Cj / K k=1 Ck the estimated capacity share of the j-th path. As soon as an AISLE station detects congestion on its primary path, it performs the following path selection procedure. Step 1: If no path swap has been performed in the past Th seconds (hysteresis time), with probability Πs =

K X

j=1 j 6= p

Cp πj = 1 − PK k=1

Ck

(2)

evaluate whether a swap should be performed (goto 2); else with probability Πns = 1 − Πs do nothing (goto 3). Step 2: If there exist some secondary paths i, whose available bandwidth estimation Bi is known and fresh enough (i.e., it was last estimated not earlier than a bandwidth decay time Td ), swap primary path p and secondary path j with j such that: Bj = maxi (Bi ) and Bj > θBp , with θ > 1. Else (i.e., if the bandwidth estimation value of all secondary available paths are unknown or stale): swap primary path p and secondary path j with probability πj as in (2); with probability Πns do nothing. Step 3: End of path swap procedure. Note that Step 1 avoids frequent swaps when Cp is large. If swapping is considered (Step 2), the estimation of the available bandwidth is used, which provides a more accurate indication than the estimation of the path capacity. However, the bandwidth estimation on the secondary paths must be fresh enough to be considered, i.e., not too much time must have elapsed since it was last estimated. Recall that the bandwidth estimation can only be accomplished on the active path, where a sustained flow of data and SACKs is available. When the bandwidth estimation is unavailable for all the secondary paths (because they have not been tested yet) or stale, the swapping decision is based on the capacity estimation only; however an extra caution is added in this case: again the path swap is allowed with a probability depending on the capacity share.

C. Path management AISLE path (and interface) management is the main focus of this paper. The advantages of using multiple wireless interfaces highlighted in [12], [29], [10] would be wasted without an efficient management of the available paths, based on relaxing SCTP’s rigid “primary-secondary” path definition. The idea at the core of AISLE path management is quite simple: every time an AISLE sender has reason to believe that the primary path it is using has become congested, it tries to determine whether it would have better luck on one of the secondary paths. A time hysteresis is introduced to avoid bouncing the data back and forth from one path to the others. Before detailing the procedure, we remark that such path management becomes especially appealing in the case of AISLE stations with attachment to different PoAs. Indeed, it is easy to see that, if too many wireless stations associate to the same PoA, it would soon become congested. If another PoA is within radio coverage of some of these stations, and if they use AISLE, they can decide to associate to the second PoA, thereby relieving the congestion on the first one. In this way, an autonomic selection of the least congested path is achieved, without the intervention of the user or of a system operator. In describing the path management procedure we make the following assumptions. (i) K ≥ 2 paths are available between two AISLE stations, and each goes through a different PoA. (ii) The wireless links are the path bottleneck, hence the capacity and available bandwidth of the path are those of the wireless link (the case where the path bottleneck is a wired link will be considered later on, while presenting the AISLE performance). (iii) For each path i, an AISLE station determines its capacity, Ci , using packet pair techniques; when path i becomes the primary, an available bandwidth estimation, Bi , is also obtained. Furthermore, we consider that AISLE infers congestion on the primary path when either of these situations occurs: 1) a packet loss is detected through retransmission timeout expiration or triple duplicate SACKs (as in the TCP congestion

V. T HE SPLIT- BY- CAPACITY

OPERATING POINT

Before evaluating the performance of AISLE, we derive in this section the split-by-capacity operating point, i.e., the optimal operating point that will be used in what follows as a reference to assess the performance of AISLE. The split-bycapacity operating point is optimal in the sense that, in the case of overlapping networks, it corresponds to the partition of users among the available networks so that the per-station throughput is fair. 5

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Let us consider K PoAs with overlapping coverages. As a first step, let us assume that there are only AISLE stations and they can connect to all of the K PoAs. Let Ni denote the number of stations P using PoAi , the total number of AISLE K stations being N = i=1 Ni . The split-by-capacity operating point, is such that: τj τi = ∀i, j (3) Ni Nj

Throughput (Mb/s)

where τi is the maximum total throughput achievable in PoAi . In order to compute τi , let us first focus on the case of overlapping 802.11 WLANs. By neglecting the total throughput reduction due to collisions at the MAC layer, the maximum achievable throughput at PoAi can be computed by evaluating the theoretical payload transmission time tpi at the maximum transmission rate Ri available at PoAi , over the theoretical overhead time toi of a successful transmission at the MAC layer due to DIFS, SIFS, RTS/CTS/ACK transmissions and post-backoff time, τi = Ri

tpi tpi + toi

0.1

0

10

20 30 40 Wireless Stations on 802.11a AP

50

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Fig. 2. Average throughput per wireless station, as the station distribution between the 802.11a and the 802.11b AP varies

assuming that (5) holds for these stations too, the maximum throughput that the AISLE stations can obtain at PoAi is now given by,

(4)

τi = αRi Ri

Ni /(αRi Ri ) Ni /(αRi Ri ) + Fi /(αGi Gi )

(7)

where the second term in the denominator accounts for the throughput reduction due to the channel sharing among stations using different transmission rates. Substituting this expression in (3) and summing over j (j = 1, . . . , K), we get PK PK K X αRj Rj j=1 αRj Rj j=1 αRj Rj Fj =N + − Fi (8) Ni G αRi Ri α αGi Gi Gj j j=1

(5)



K X



αR Rj αR Ri αR Ri N + Ni = PK i Fj j  − Fi i (9) G α α Gj j Gi Gi j=1 αRj Rj j=1

Then, substituting (5) in (3) and summing over j (j = 1, . . . , K), we get αR Ri Ni = N PK i j=1 αRj Rj

1

0.01

Note that the factor tpi /(tpi + toi ) accounts for the intrinsic inefficiency of the access mechanism and depends on the specific technology. As an example, let us compute tpi and toi in the case of an 802.11a PoA at 54 Mbps and of an 802.11b PoA at 1 Mbps. Considering the transmission of two back-toback SCTP chunks of 1500 bytes followed by an SCTP ACK between the AP and one station, without any other contending station, we obtain τi = 0.359Ri and τi = 0.859Ri for the 802.11a and the 802.11b PoA, respectively. By defining αRi = tpi /(tpi + toi ), we can rewrite (4) as τi = αRi Ri

802.11a 802.11b N=10 N=20 N=40 N=60

10

When a PoA uses the UMTS technology (see the results reported in Sections VIII), the maximum throughput achievable with the UMTS technology corresponds to the available bandwidth of the common transport channels (set in our simulations to 384 Kbps or 2 Mbps) and the optimal, fair node distribution can be derived similarly to what done above.

(6)

The validity of this simple result is confirmed by a set of simulations. Figure 2 shows the average per-station throughput when only single-interface stations are considered, K = 2 and PoA1 and PoA2 use, respectively, the 802.11a and the 802.11b technology. The stations data rate is equal to 54 Mbps when using the PoA1 and to 11 Mbps when using the PoA2 . The plot presents the per-station throughput of the users transmitting to PoA1 as the number of stations, N1 , varies from 1 to 60 (solid line), and the throughput of the N −N1 stations using the PoA2 . Given a value of N , the desirable, fair node distribution lies at each intersection between the 802.11a and the 802.11b curves, where condition (3) holds, i.e., where all stations on both WLANs have the same throughput. The theoretical values of N1 , corresponding to the optimal fair node distribution and computed through (6), are represented by the vertical solid lines. We note that an excellent match exists between simulation and analytical results. Next, consider the case where, in addition to the N AISLE stations, Fi non-AISLE stations operate at rate Gi at PoAi . By

VI. M EETING ZONE WITH

OVERLAPPING

WLAN S

In this section, we consider the mixed wired-wireless scenario shown in the left-hand side scheme of Figure 1, which represents a meeting zone where some overlapping wireless coverages are generated by different PoAs. In particular, through the ns2 simulator we study the case of 802.11-based WLAN PoAs using the Distributed Coordination Function (DCF) at the MAC layer. The DCF parameters are set to the standard values; RTS/CTS is used for payloads in excess of 400 bytes and the Short Retry Limit and the Long Retry Limit are set to 7 and 4, respectively. The link-layer queues at the wireless stations are 50 data frame long, while the AP queues accommodate up to 400 data frames. We assume that the wireless channel is error-free (thus packet losses are due to either buffer overflow at the AP or channel contention) and 6

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that the propagation delay is negligible on the wireless part of the network. The number N of wireless nodes operating within the whole network area is a varying parameter in our simulations. We consider N FTP sources, N/2 at the S1 node and N/2 at the S2 node (see Figure 1), generating long-lived flows, transferred through the Internet to the wireless stations. The packet size at the IP level is 1500 bytes. Regarding specific AISLE parameters, we set the threshold θ to 1.1 and the hysteresis time Th to 60 s; we investigated several values of the bandwidth decay factor Td , as shown below, and a choice of Td = 120 s is made for most of the simulation runs, unless otherwise stated. In Section VI-A we study a static scenario in which the number of stations is constant, while, in order to evaluate how AISLE adapts to a dynamic scenario, in Section VI-B the number of stations arriving or leaving the meeting zone varies with time. In Section VI-C, we study the effects of the AISLE mechanism when there are both single- and multiinterface stations connected to one or more 802.11 WLANs (clearly, single-interface stations are connected to one AP only and do not exploit the SCTP multihoming feature). Finally, Section VI-D addresses the case where more than two PoAs provide coverage over the same geographical area.

Wireless Stations per 802.11a AP

70

Ref. Node Distr. N=10 N=20 N=40 N=60

60 50 40 30 20 10 0 0

500

1000 Time (s)

1500

2000

Fig. 3. Time evolution of the number of wireless stations using the 802.11a AP as a function of time, for different total number of users. All stations are initially associated to the 802.11b AP

Wireless Stations per 802.11a AP

60

A. Static scenario We assume that each station has two radio interfaces and that two PoAs are available, namely PoA1 and PoA2 . Stations can connect simultaneously to PoA1 and PoA2 , which operate under the 802.11a and the 802.11b standards, respectively. Also, stations using the path through PoA1 as primary transmit at 54 Mbps, while stations selecting the 802.11b interface transmit at 11 Mbps. We first consider the case in which the system bottleneck is represented by the wireless portion of the network, since the fixed nodes, S1 and S2 , are connected to the APs (namely, PoA1 and PoA2 ) through a wired link of 100 Mbps speed. The total number of stations N is fixed throughout the simulation (namely, N = 10, 20, 40, 60). In particular, we evaluate the AISLE capability of reaching the optimal, fair split-by-capacity operating point identified in the previous section. Figures 3 and 4 show how the wireless stations selfdistribute between the APs, starting from the situation where all nodes use the 802.11b AP (PoA2 ). Figure 3 presents the number of stations using the 802.11a AP as a function of the simulation time and compares the AISLE performance with the optimal, fair split-by-capacity operating point, represented by the horizontal lines. The parameter Td , that is the bandwidth decay time over the secondary path, is set to 120 s. At time 0 s all nodes are using PoA2 ; however, through the AISLE mechanism, they rapidly choose the radio interface to use, approaching the reference value and yielding a stable partition of stations. In Figure 4 we analyze the effect of parameter Td on the node partition for N = 10, 60. In both cases, Td does not significantly affect the node distribution between the APs; thus, the remaining results in this subsection will be presented by fixing Td at 120 s.

50

40 N=60 Td=60s Td=120s Td=240s N=10 Td=60s Td=120s Td=240s

30

20

10

0 0

500

1000 Time (s)

1500

2000

Fig. 4. Time evolution of the number of wireless stations using the 802.11a AP for different values of the bandwidth decay parameter Td , and 10 or 60 users

We now vary the initial condition, and assume that at time 0 s all stations use the 802.11a WLAN through PoA1 . The evolution of the number of nodes using the PoA1 with time, not shown here for the sake of brevity, is similar to the one in Figure 3, and the AISLE station distribution is, at steady-state, again very close to the optimal, fair value. Table I reports the average per-station throughput and the Jain’s fairness index of the station throughput computed over all nodes, when all stations initially use either the 802.11a or 802.11b PoA. As expected, the throughput is inversely proportional to the number of wireless stations, since the capacity is shared among all connections. The results show that, independently of the initial conditions, AISLE autonomously and optimally selects the radio interface that maximizes the per-station throughput in a fair manner. The impact of the initial condition is negligible and the achieved throughput is, on average, equal on both paths. This behavior is confirmed by the values of Jain’s fairness index: the fairness achieved is remarkably high with any number N of stations. Figure 5 presents the average time between two consecutive path swaps as well as the 5-th and the 95-th percentiles of its probability distribution, as the total number of stations varies 7

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TABLE I AVERAGE P ER - STATION T HROUGHPUT AND JAIN ’ S T HROUGHPUT FAIRNESS I NDEX

N All nodes start on 802.11a All nodes start on 802.11b

10 1.945 1.979

Throughput [Mbps] 20 40 60 0.976 0.472 0.308 0.975 0.476 0.311

Fairness index 20 40 0.9797 0.9887 0.9841 0.9861

60 0.9920 0.9931

70

2000

N=10 wireless bottleneck wired bottleneck

Initial population on 802.11a Initial population on 802.11b

1800

N=60 wireless bottleneck wired bottleneck

60

1600 Wireless Stations per 802.11a AP

Time between Path Swap (s)

10 0.9899 0.9925

1400 1200 1000 800 600 400 200

50

40

30

20

10

10

20

40

60

Wireless Stations

0 0

Fig. 5. Time between two consecutive path swaps as a function of the total number of stations, for different initial conditions

500

1000

1500

2000

Time (s)

Fig. 6. Time evolution of the number of wireless stations using the 802.11a AP as a function of time, for 10 and 60 users, when half of the connections experience a bottleneck on the wired or wireless part of the network. All stations are initially associated to the 802.11b AP

and for different initial conditions. It is interesting to notice that changes are more frequent when nodes start to transmit at 54 Mbps data rate. Indeed, when all nodes are initially using the PoA2 and start transmitting at the lower data rate, the majority of them likely try to swap interface. In this case, since AISLE favors the node transfer toward the higher data rate AP, very few nodes will switch back to the previously used channel. Rather counter-intuitively, when all nodes are initially associated to the higher-capacity AP, they may try to swap interface: indeed, our path management algorithm allows nodes to perform a statistical AP swap, proportional to the estimated capacities. In this case, many of the initial path swaps are unfavorable, and the nodes swap again trying to increase their perceived throughput. Finally, it is important to stress that, regardless of the number of nodes and of the initial scenario, stations never bounce between the two access technologies: in the worst case, a node waits little more than 10 minutes (on average) before swapping. So far we have assumed that the wireless links are the system bottleneck. Now, we investigate how a bottleneck on the wired part of the network impacts the path selection. Consider the fixed node S1 to be connected to the PoA1 with a wired link of 100 Mbps speed; S2 instead is connected to the PoA2 with a wired link of 2 Mbps, thus being the bottleneck for the N/2 flows generated at the S2 source. Figure 6 presents the temporal evolution of the number of stations using the 802.11a AP, when N = 10 (lines with triangles) and N = 60 (lines with circles), as well as the number of flows transmitted through the 802.11a AP, among those originated at S1 (labeled by ‘wireless bottleneck’) and those originated at S2 (labeled by ‘wired bottleneck’). For N = 60, all stations throttled by the wired bottleneck

TABLE II AVERAGE P ER - STATION T HROUGHPUT AND FAIRNESS IN PRESENCE OF A BOTTLENECK IN THE WIRED NETWORK

N 10 20 40 60

Throughput [kbps] S1 S2 3485 376 1754 182 860 89 562 58

Fairness S1 S2 0.962 0.999 0.976 0.998 0.985 0.996 0.979 0.998

detect a bandwidth no larger than 67 Kbps (i.e., 2Mbps shared among 30 stations). As shown in the figure, at steady state all of these stations select the 802.11a AP, while only 20 out of 30 stations throttled by the wireless bottleneck use this AP. Even if connections originated at S2 and experiencing the bottleneck on the wired network are unable to detect any difference between the APs (thus failing in the AISLE path selection), the connections originated at S1 are able to identify the congested AP and correctly react, thus counterbalancing the misbehavior of the connections throttled by the wired bottleneck. Clearly, when all traffic flows come across a bottleneck in the wired part of the network, none of the AISLE stations will be able to differentiate between the PoAs: all nodes will evenly redistribute themselves between the PoAs, independently of the capacity and the congestion level of the wireless network. Finally, Table II reports the per-station throughput and fairness achieved by the stations connected to the source S1 through the 100 Mbps link, as well by those connected to S2 through the 2 Mbps link. As expected, the throughput of stations connected to S2 is much lower, since it is affected 8

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45

45 Ref. Node Distr. 802.11a 802.11b

35 30 802.11a

25

Ref. Node Distr. 802.11a 802.11b

40 Wireless Stations per AP

Wireless Stations per AP

40

20 802.11b 15 10 5

35 30 25 802.11b 20

802.11a 15 10 5

0

0 0

500

1000

1500 Time (s)

2000

2500

3000

0

Fig. 7. Time evolution of the number of wireless stations using the 802.11a and the 802.11b APs

500

1000 1500 Time (s)

2000

2500

Fig. 8. Time evolution of the number of wireless stations using the 802.11a and the 802.11b APs

70 Wireless Stations per 802.11a AP

by the wired bottleneck whose capacity is shared among N/2 flows. The throughput of stations connected to S1 is instead limited by the radio channel bandwidth. The values of Jain’s fairness index, computed separately for the two sets of stations, confirm the excellent throughput fairness perceived by stations sharing the same bottleneck. B. Dynamic scenario We investigate here the capability of AISLE nodes to create, in a dynamic and autonomic manner, a network topology where users arriving or leaving the meeting zone are optimally distributed among the available PoAs. First, still referring to the left-hand side network in Figure 1, we consider the case of two PoAs using the WLAN technology. Also, we consider that users reach the meeting zone and connect one by one to the provided WLANs. Again, AP1 and AP2 operate under 802.11a and 802.11b, respectively, and all stations using the path through AP1 as primary transmit at 54 Mbps, while stations selecting the 802.11b interface transmit at 11 Mbps. The station interarrival time is a random variable exponentially distributed with mean value equal to 30 s. A maximum number of 50 wireless nodes is considered and every station, upon arrival, randomly selects one of the APs with equal probability. Figure 7 shows the time evolution of the number of stations using the 802.11a and the 802.11b APs. Even if, initially, every station randomly chooses the AP to transmit to, nodes do not remain equally distributed between the APs. Indeed, an AISLE node spontaneously tends to use the interface that maximizes its throughput; thus, in the considered network scenario, a larger number of stations will associate to the 802.11a AP, whose capacity is larger. At any given time, the reference value (solid line) is obtained as the split-by-capacity operating point. These results clearly show that AISLE stations rapidly adapt to changes in the network topology, and optimally choose the radio interface. Next, we consider the case of users leaving a meeting zone, i.e., a network where the number of stations decreases with time. In Figure 8 at the beginning of the simulation there are 50 wireless stations, which are fairly distributed between the APs

Ref. Node Distr. AISLE Users = 20 AISLE Users = 40 AISLE Users = 60

60 50 40 30 20 10 0 0

500

1000 Time (s)

1500

2000

Fig. 9. Time evolution of the number of wireless stations using the 802.11a AP, with five single-interface stations and different number of AISLE stations in the system

according to the split-by-capacity values: 41.5 on the 802.11a AP, and 8.5 on the 802.11b AP, which were rounded off to 42 and 8, respectively. Then, starting from 500 s, on average one station every 30 s moves out of the network. We observe that also in this case the network is efficiently rearranged, and the nodes partitioning obtained through AISLE is close to the dynamically changing split-by-capacity reference value (indicated by solid lines). Clearly, the fewer the nodes, the fewer the losses and, thus, the fewer the necessary node swaps. Finally, it is worth mentioning that the results presented in this section have been derived with a bandwidth decay time Td = 120 s. However, using Td equal to 15, 30 or 60 (i.e., respectively smaller, equal and larger than the average station interarrival/departure time) yielded similar simulation results. C. Single- and multi-interface stations In the scenarios considered so far, all nodes adopt the AISLE mechanism. In this section, instead, we consider AISLE stations coexisting with single-interface stations that connect to one AP only. This situation allows us to evaluate the effectiveness of AISLE in realistic scenarios, where only some stations adopt the proposed mechanism. 9

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50

50 AP1 AP2 AP3

N=60 40 Wireless Stations per 802.11a AP

Wireless Stations per 802.11b AP

40

30 N=60

20

N=20

10

AP1 AP2 AP3

30

20 N=20 N=60

10

N=20 0

0 0

500

1000 Time (s)

1500

2000

0

Fig. 10. Time evolution of the number of wireless stations using each PoA, when three 802.11b APs are available

N 10 20 40 60

2000

AISLE Throughput Fairness 1.630 0.948 0.775 0.973 0.323 0.972 0.246 0.982

SNR-based Throughput Fairness 1.620 0.698 0.787 0.696 0.380 0.697 0.254 0.684

as those proposed in [30], [31], which are implemented at the MAC layer and based on the evaluation of the signal-to-noise ratio (SNR): the interface with the largest SNR is selected. We consider that two PoAs are available, providing coverage on the same geographical area and using the 802.11a and 802.11b technology, respectively. We assume that N stations are uniformly distributed in the area covered by the two PoAs. In both the cases of AISLE and the SNR-based scheme, stations set their data rate according to their distance from the PoA (hence, the SNR value). Table III compares the performances obtained through AISLE and the SNR-based scheme, in terms of average perstation throughput and fairness. We can see that the two schemes provide on average similar values of per-station throughput, however AISLE significantly outperforms the SNR-based approach in terms of fairness. Indeed, users are uniformly distributed over the network area; it follows that, when the SNR-based approach is applied, on average half of the stations use the 802.11b PoA, achieving much lower throughput than the stations using the 802.11a PoA.

In this section, we consider the case of more than two PoAs providing coverage over the same area. In particular, we study two scenarios. In the first one, three 802.11b PoAs are available and we consider N =20 and N =60 stations. The time evolution of the number of wireless stations using each PoA is reported in Fig. 10. As expected, all PoAs tend to have the same number of connected stations. Despite all stations being initially connected to the same PoA (labeled by AP1 in the figure), in a very short time the stations evenly distribute according to the split-by-capacity operating point (reported in the plot for reference). The second scenario includes two 802.11b and one 802.11a APs; nodes transmit at 54 Mbps toward the 802.11a AP (namely, AP3 ) and at 11 Mbps toward the 802.11b APs. Fig. 11 reports the time evolution of the number of wireless stations connected to each of the AP, when the total number of stations is 20 and 60. Again, the stations distribute among the three APs according to the split-bycapacity operating point (reported in the figure for reference). SNR- BASED

1500

TABLE III AVERAGE PER - STATION THROUGHPUT AND FAIRNESS : AISLE VS . AN SNR- BASED APPROACH

D. Three PoAs

AGAINST AN

1000 Time (s)

Fig. 11. Time evolution of the number of wireless stations using each PoA, when two 802.11b and one 802.11a APs are available

Let us consider two APs using different technologies (namely, AP1 and AP2 adopt the 802.11a and the 802.11b standard, respectively). Consider 20, 40 and 60 AISLE users, transmitting at 54 Mbps toward AP1 and at 11 Mbps toward AP2 , and initially using AP1 . Furthermore, assume that five single-interface stations are connected to the 802.11a AP and transmit at a data rate of 6 Mbps. Figure 9 shows the number of AISLE stations using the 802.11a AP as a function of the simulation time. The impact of the presence of the single-interface stations connected to the 802.11a AP is that fewer AISLE stations than before transmit toward this AP. The split-by-capacity node distribution is obtained from (9) by setting F1 = 5, G1 = 6 Mbps, and F2 = 0. We highlight that also in this case there is an excellent agreement between simulation results and the optimal, fair node distribution.

VII. C OMPARISON

500

VIII. H ETEROGENEOUS

NETWORKS :

WLAN

AND

UMTS

Here, we consider the case of heterogeneous access networks, based on 802.11 and UMTS. The 3G cellular network, simulated with EURANE (Enhanced UMTS Radio Access Network Extensions for ns-2) [33], includes both the UMTS Radio Access Network (UTRAN), that handles all the radiorelated functionalities, and the Core Network, which is responsible for routing connections to external networks. The UTRAN consists of Radio Networks Subsystems (RNSs),

SCHEME

The objective of this section is to compare AISLE against a different interface selection scheme. We consider a solution 10

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20 N=10 N=20 N=30

25

Wireless Stations using the 802.11b APs

Wireless Stations using the 802.11b AP

30

20

15

10

5

0

AP1 AP2 15

10

5

0 0

500

1000 Time (s)

1500

2000

0

Fig. 12. Time evolution of the number of wireless stations using the 802.11b AP, for different total numbers of users. Heterogeneous network scenario, with all stations being initially associated to the WLAN

500

1000 Time (s)

1500

2000

Fig. 13. Time evolution of the number of wireless stations using the 802.11b APs

also to AP1 , while the other half are connected to AP2 . Initially, all fixed stations use their WLAN PoA. In addition, there are 10 mobile stations that are initially associated to AP1 and to the UMTS Node B, and then, at time 600 s, start moving toward AP2 , leaving the WLAN coverage zone. The node speed is set to 10 m/s and the AP2 is 2 Km away from AP1 ; the departure time of each station is a random variable exponentially distributed with mean value 5 s. Figure 13 reports the temporal evolution of the number of stations using the 802.11 APs: the solid lines represent the number of stations using AP1 , while the dashed line the number of stations using AP2 . The horizontal lines again correspond to the optimal, fair node distribution between WLAN and UMTS when 15 or 5 stations are associated to one AP and the 3G PoA. At time 0 s, 15 stations use AP1 (5 fixed and 10 mobile), while 5 fixed stations use AP2 . From then on, they re-distribute in a fair manner between the WLAN and the UMTS PoAs (from time 0 s to 600 s). From 600 s onwards, the 10 mobile stations start to move away from AP1 : both the stations using AP1 and those using the UMTS Node B declare AP1 as inactive after 5 timeouts expired for either data or Heartbeat packets (from 600 s to 1100 s). From 1100 s to 1500 s all mobile stations are outside the coverage of both the WLAN APs and are all using the UMTS PoA. From 1500 s on, the mobile stations start to enter the AP2 coverage: after the association, most of them leave the congested UMTS PoA to use the 802.11 AP2 . We remark that the autonomic selection of the best interface performed by AISLE enables users to move from one WLAN coverage to another, through the UMTS umbrella cell, without connectivity disruption.

including one Radio Network Controller and one or more Node Bs. Considering the Radio Link Control (RLC) protocol, we use the Acknowledged Mode, normally selected for web browsing and email downloading. At the physical layer, we use common transport channels: every channel is shared among all users within a cell [34]. We set the Transmission Time Interval (TTI) of the physical channels to 10 ms, the RLC payload size to 40 bytes, and the bandwidth to 384 Kbps or 2 Mbps, depending on the simulation scenario. Note that we do not consider the case where UMTS users are assigned dedicated channels, since in this case the use of a primary and secondary path would lead to waste of wireless resources. A. Static scenario We evaluate how the wireless stations distribute among overlapping heterogeneous networks, again referring to the left-hand side of Figure 1 when two PoAs are available: PoA1 is an 802.11b AP, while PoA2 is a UMTS Node B. Stations using the 802.11b AP transmit at 11 Mbps, while stations using the UMTS Node B employ a channel at 2 Mbps. All stations implement AISLE and they initially use the WLAN PoA. Figure 12 presents the number of stations using the 802.11 AP, as a function time, for N =10, 20 and 30 stations. Again, user partition is very close to the optimal, fair value. Indeed, even if on the UMTS transport channels the transmission is scheduled in radio frames (TTI of 10 ms), affecting the accuracy of packet-pair capacity estimation, nevertheless the bandwidth estimation technique accurately evaluates the available bandwidth, allowing an optimal node distribution.

IX. C ONCLUSIONS B. Dynamic scenario with mobile users

We defined and investigated AISLE, a novel high-level protocol for multi-interface wireless nodes that autonomically chooses to use the best Point of Access within radio coverage. Our aim was to offer users the “best connection” in homogeneous as well as heterogeneous overlapping network environments. AISLE operates on top of a multihoming transport-layer protocol and is therefore independent of the technologies used at the MAC and physical layers. It selects

We now refer to the dynamic scenario in the right-hand side of Figure 1: a UMTS umbrella cell covers the area of two disjoint WLANs equipped with 802.11b APs. Stations using the 802.11b AP transmit at 11 Mbps, while stations using the 3G network transmit on a 384 Kbps channel. We consider 10 fixed multi-interface stations, all connected to the 3G cellular network. Furthermore, half of them are connected 11

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the radio interface that maximizes the node throughput, given that fairness is guaranteed, and, thanks to the accurate bandwidth estimate on the existing paths, it dynamically adapts to network environment changes. While deriving performance results, we considered IEEE 802.11 a/b as well as UMTS Points of Access. After having analytically derived the optimal distribution of stations among the points of access, we showed by simulation that wireless stations adopting AISLE not only maximize their own throughput in presence of multiple overlapping coverages, but they also optimally distribute across overlapping wireless networks.

[22] S. Shakkottai, E. Altman, and A. Kumar, “The Case for Non-cooperative Multihoming of users to Access Points in IEEE 802.11 WLANs,” IEEE INFOCOM, Apr. 2006. [23] A. Jardosh, K. Mittal, K. Ramachandran, E. Belding, and K. Almeroth, “IQU: Practical Queue-Based User Association Management for WLANs - Case Studies, Architecture, and Implementation,” ACM MobiCom, Sept. 2006. [24] R. Fracchia, C. Casetti, C.-F. Chiasserini, and M. Meo, “A WiSE Extension of SCTP to Wireless Networks,” IEEE ICC, May 2005. [25] J. C. Bolot, “End-to-end Packet Delay and Loss Behaviour in the Internet,” ACM SIGCOMM Computer Communication Review, Vol. 23, No. 4, Oct. 1993. [26] R. L. Carter and M. E. Crovella, “Measuring Bottleneck Link Speed in Packet-switched Networks,” Technical Report, 1996. [27] S. Saroiu, P. K. Gummadi, and S. D. Gribble, “SProbe: A Fast Technique for Measuring Bottleneck Bandwidth in Uncooperative Environments,” IEEE INFOCOM, June 2002. [28] L. A. Grieco and S. Mascolo, “End-to-End Bandwidth Estimation for Congestion Control in Packet Networks,” IEEE QoS-IP, Feb. 2003. [29] K. Chebrolu and R. R. Rao, “Communication Using Multiple Wireless Interfaces” IEEE WCNC, Mar. 2002. [30] G. P. Pollini, “Trends in Handover Design,” IEEE Communication Magazine, Mar. 1996. [31] S. Balasubramaniam and J. Indulska, “Vertical Handover Supporting Pervasive Computing in Future Wireless Networks,” Computer Communications, Vol. 27, No. 8, May 2004. [32] P. Nain, D. Towsley, B. Liu, and Z. Liu, “Properties of Random Direction Model,” IEEE INFOCOM, Mar. 2005. [33] EURANE, Enhanced UMTS Radio Access Network Extensions for ns-2, http://www.ti-wmc.nl/eurane/ [34] WCDMA for UMTS: Radio Access for Third Generation Mobile Communications, Eds. H. Holma and A. Toskala, 3rd Ed., J. Wiley and Sons, 2004.

R EFERENCES [1] C. Shen, D. Pesch, and J. Irvine, “A Framework for Self-Management of Hybrid Wireless Networks Using Autonomic Computing Principles,” IEEE Communication Networks and Services Research Conference, May 2005, pp. 261–266. [2] R. Stewart, “Stream Control Transmission Protocol,” IETF RFC 2960, Oct. 2000. [3] S. Mascolo, C. Casetti, M. Gerla, M. Y. Sanadidi, and R. Wang, “TCP Westwood: Bandwidth Estimation for Enhanced Transport over Wireless Links,” ACM SIGMOBILE, July 2001. [4] R. Stewart et al., “RFC 3758: Stream Control Transmission Protocol (SCTP) — Partial Reliability extension,” IETF, May 2004. [5] J. Shi, Y. Jin, H. Huang, and D. Zhang, “Performance Evaluation of SCTP as a Transport Layer Solution for Wireless Multi-access Networks,” IEEE WCNC, Mar. 2004. [6] A. Argyriou and V. Madisetti, “Performance Evaluation and Optimization of SCTP in Wireless Ad-hoc Networks,” IEEE LCN, Oct. 2003. [7] J. R. Iyengar, K. C. Shah, P. D. Amer, and R. Stewart, “Concurrent Multipath Transfer Using SCTP Multihoming,” SPECTS, July 2004. [8] A. A. El Al, T. N. Saadawi, and M. Lee, “LS-SCTP: A Bandwidth Aggregation Technique for Stream Control Transmission Protocol,” Computer Communications, Vol. 27, No. 10, 2004, pp. 1012–1024. [9] T. Goff, D. S. Phatak, “Unified Transport Layer Support for Data Striping and Host Mobility,” IEEE Journal on Selected Areas in Communications, Vol. 22, No. 4, May 2004, pp. 737–746. [10] S. Kashihara, T. Nishiyama, and K. Iida, “Path Selection Using Active Measurement in Multihomed Wireless Networks,” IEEE SAINT, Jan. 2004. [11] S. Joo Koh, M. Jeong Chang, and M. Lee, “mSCTP for Soft Handover in Transport Layer,” IEEE Communications Letters, Vol. 8, No. 3, Mar. 2004, pp. 189–191. [12] L. Ma, F. Yu, and V. C. M. Leung, “A New Method to Support UMTS/WLAN Vertical Handover Using SCTP,” IEEE Wireless Communications, Aug. 2004. [13] L. Ma, F. Yu, V. C. M. Leung, and T. Randhawa, “SMART-FRX: A Novel Error-recovery Scheme to Improve Performance of Mobile SCTP during WLAN to Cellular Forced Vertical Handover,” IEEE WCNC, Mar. 2005. [14] J. Ylitalo, T. Jokikyyny, T. Kauppinen, A.J. Tuominen, J. Laine, “Dynamic Network Interface Selection in Multihomed Mobile Hosts,” 36th Annual Hawaii International Conference on System Sciences (HICSS), Jan. 2003. [15] M. Riegel, M. Tuxen, N. Rozic, D. Begusic, “Mobile SCTP Transport Layer Mobility Management for the Internet,” SoftCOM, Oct. 2002. [16] C. Guo, Z. Guo, Q. Zhang, and W. Zhu, “A Seamless and Proactive Endto-end Mobility Solution for Roaming across Heterogeneous Wireless Networks,” IEEE Journal on Selected Areas in Communications, Vol. 22, No. 5, June 2004, pp. 834–848. [17] S.-E. Kim and J. A. Copeland, “TCP for Seamless Vertical Handoff in Hybrid Mobile Data Networks,” IEEE GLOBECOM, Dec. 2003. [18] E. Gustafsson and A. Jonsson, “Always Best Connected,” IEEE Wireless Communications, Vol. 10, No. 1, Feb. 2003, pp. 49–55. [19] S. Vasudevan, K. Papagiannaki, C. Diot, J. Kurose, and D. Towsley, “Facilitating Access Point Selection in IEEE 802.11 Wireless Networks,” ACM Internet Measurement Conference, Oct. 2005. [20] G. A. Di Caro, S. Giordano, M. Kulig, D. Lenzarini, A. Puiatti, and F. Schwitter “A Cross-Layering and Autonomic Approach to Optimized Seamless Handover,” WONS, Jan. 2006. [21] G. Tan and J. Guttag, “The 802.11 MAC Protocol Leads to Inefficient Equilibria,” IEEE INFOCOM, Mar. 2005.

Claudio Casetti (M’05) graduated in Electrical Engineering from Politecnico di Torino in 1992 and received his PhD in Electronic Engineering from the same institution in 1997. In 1995, he was a visiting scholar with the Networks Group of the University of Massachusetts, Amherst. In 2000, he was a visiting scholar with the Networking Group at UCLA. He is an Assistant Professor at the Dipartimento di Elettronica e Telecomunicazioni of Politecnico di Torino. He has coauthored more than 80 journal and conference papers in the fields of networking and holds three patents. His interests focus on performance evaluation of TCP/IP networks and wireless communications. He is a member of IEEE.

Carla-Fabiana Chiasserini graduated with a summa cum laude degree in Electrical Engineering from the University of Florence in 1996. She did her graduate work at the Politecnico di Torino, Italy, receiving the Ph.D. degree in 1999. Since then she has been with the department of Electrical Engineering at Politecnico di Torino, where she is currently an Associate Professor. Since 1999, she has worked as a visiting researcher at the University of California, San Diego, California. Her research interests include architectures, protocols and performance analysis of wireless networks for integrated multi-media services. She is a member of the editorial board of the Ad Hoc Networks Journal (Elsevier), and has served as an associate editor of the IEEE Communications Letters since 2004.

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Roberta Fracchia graduated with summa cum laude from Politecnico di Torino, in Telecommunications Engineering in July 2002. In 2003-2004 she was a visiting researcher in the Information Systems Networking Lab at the Electrical Engineering Department of Stanford University. In 2006 she obtained the Ph.D. degree at Politecnico di Torino. In December 2006 she joined Motorola Labs in Paris where she is currently a senior research engineer. Her research interescts include performance evaluation of transport layer protocols, architecture and Radio Resource Management of next generation wireless networks.

Michela Meo received the Laurea degree in Electronic Engineering in 1993 and the Ph.D. degree in Electronic and Telecommunication Engineering in 1997, both from Politecnico di Torino. Since November 1999, she is a Professor at the same institution. She coauthored more than ninety papers, more than thirty of which on international journals and she authored two book chapters. She edited six special issues of international journals, including ACM Mobile Networks and Applications (MONET) and Performance Evaluation Journal. She was chair of three editions of ACM MSWiM (International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems) and of the IEEE International Workshop on QoS in Multiservice IP Networks (QoS-IP). Her research interests are in the field of analysis and dimensioning of wireless and inter-vehicular networks, performance evaluation of transport and link layer protocols, traffic measurement and characterization.

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