A Layer-2 Trigger to Improve QoS in Content and Session-Oriented Mobile Services ∗
Emiliano Casalicchio, Valeria Cardellini, Salvatore Tucci Dep. of Computer Science Systems and Production University of Roma “Tor Vergata” Roma, Italy {casalicchio,cardellini}@ing.uniroma2.it,
[email protected]
ABSTRACT
1. INTRODUCTION
In present wireless networks, mobile users frequently access continuous and session-oriented Internet services. During the handover, the management of session-related information introduces additional overheads and delays, due to context transfer procedures. Such delays may affect the QoS perceived by mobile users, making more difficult to realize seamless handover procedures. In this paper, we propose a framework to design a layer-2 trigger on the mobile node that intelligently activates the Context Transfer Protocol (CTP). Our proposed solution is based on methods to forecast the handoff time of the mobile node and the access router that will handoff the connection, and a model to estimate the time needed to complete the context transfer procedure. We show that the forecasting algorithm is stable, is able to effectively avoid the ping-pong effect, and converges both for simple and complex trajectories, typical of urban regions. We also evaluate the performance of CTP in the real case of a GSM network.
Nowadays, Internet services like VoIP, multimedia streaming, on-line games, on-line transactions, and many content delivery related services can be accessed through broadband wireless networks. All these services are session-oriented, delay-bounded, and context sensitive and are characterized by a service level agreement (SLA) that must be honored. When users move across different network coverage areas managed by diverse access routers, the broadband connectivity is not sufficient to fulfill Quality of Service (QoS) requirements, but are also required mechanisms to avoid service disruption in critical phases and to seamlessly hand off the connection from an access router to another. The fast handover mechanism [9], which was introduced to reduce the packet losses during handovers, needs to be enhanced with proper mechanisms to preserve the session continuity. In continuous and session-oriented services, handover is not only a matter of keeping a connection alive during user movements, but also of transferring the necessary information to avoid the session re-establishment every time the user reaches a new access router. We refer to context as to all the information necessary to keep alive a session and maintain the proper QoS level. The CTP provides a solution to the repetition from scratch of the service initiation message flow in the session re-establishment [2, 11]. The works in [1, 8] discuss the motivation for context transfer and present the most relevant cases for its application. If handoff procedures are conducted without transferring any context related information, session parameters (both at application and network layers) should be re-defined from scratch whenever the mobile host reaches a new access router. The re-negotiation of these parameters is complex and may require a longer time than that needed to perform the handover. Therefore, the best solution is to transfer the context from the access router of the area from which the mobile node comes to the access router of the area targeted by the mobile node. The goal of this paper is to provide a framework to activate and complete the context transfer before the handoff is terminated, in such a way as to have enough useful information at the new access router to avoid session disruption, to reduce the handoff delays, and to maintain the proper QoS level. The proposed framework is based on layer-2 trigger that recognizes when the handoff comes on and forecasts the new access router so to anticipate the context transfer. This solution is alternative to the periodical mutual exchange among neighboring access routers of context related
Categories and Subject Descriptors C.4 [Computer Systems Organization]: Performance of Systems; C.2.1 [Computer-Communication Networks]: Network Architecture and Design—Wireless communication, Network communications
General Terms Performance, Design
Keywords Mobile Services, Handoff Management, Mobile IP, Context Transfer ∗Prof. S.Tucci is currently the head of the Ufficio per l’Informatica, Presidenza del Consiglio dei Ministri, Roma, Italy
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information during the ongoing session. This latter approach has the drawback to waste bandwidth and does not ensure that the information exchanges are updated. The remaining of this paper is organized as follows. Section 2 describes the mobility management mechanism in Mobile IPv6 [7, 5] and the interaction between CTP and the fast handover mechanism. Section 3 proposes our solution to forecast the context transfer. Section 4 presents the performance evaluation of the estimation and ping-pong avoidance algorithms and the application of our framework to a GSM network. Finally, Section 5 concludes the paper.
2.
MOBILITY MANAGEMENT AND CONTEXT TRANSFER PROTOCOL
To design the layer-2 trigger, we have to consider the interaction between CTP and the underlying mobility management protocol. We consider only handovers that need to be managed at the network layer (e.g., not analyzing handovers that occur only at the data link layer), focusing on some relevant aspects of IPv6 and its fast handover mechanism [9]. The mobile node (MN) uses the auto-configuration procedure [13, 16] to obtain a new care of address (nCOA) when it enters a new network. When fast handover is applied, the current access router (AR) not only broadcasts its advertisement but also relays advertisements from neighboring ARs through a Proxy Router Advertisement message (PrRtAdv), periodically broadcasted or sent out as an answer for a Proxy Router Solicitation message (PrRtSol). The latter is issued by a mobile node when it detects through some layer-2 indicator that an handoff is likely to occur. The MN communicates its nCOA to the current AR through a Fast Binding Update (FBU) message, so that a layer-3 tunnel between the current AR, which is going to become the previous access router (pAR), and the new AR (nAR) can be established. This bidirectional tunnel is used to route packets from the pAR to the nAR. It is worthwhile to note that the whole fast handover mechanism can be applied only if the wireless interface is provided with an indicator of layer-2 events, such as the discovery of a new AR or the degradation of the signal quality to the current AR. When the mobile node moves to the nAR, all context related data have to be transferred from the pAR and not obtained by an additional message exchange between the nAR and the mobile node. This approach allows to avoid unnecessary bursts of data packets as the node gets connected to the nAR and to minimize the number of application data packets which cannot be processed properly due the lack of context related information. The Context Transfer Protocol can be initiated by the mobile node itself [11]. When the mobile node starts the handover at the data link layer, a trigger arises so that the mobile node sends the Context Transfer Activate Request (CTAR) message to the nAR, which in turn issues a Context Transfer Request (CTR) message to the pAR and receives from it the Context Transfer Data (CTD) message. In this paper, we consider that the mobile node forecasts the access router that it is going to approach and initiates the context transfer in such a way as to transfer all the session and service related information before the handoff occurs.
3. CONTEXT TRANSFER FORECASTING FRAMEWORK In this paper we design a framework that allows a mobile node to activate the context transfer procedure long enough before the network layer handoff is detected, thus to have useful context related information at the new access router when the handoff procedure is executed and the session parameters must be re-established. The framework should also: (i) ensure that the context transfer is not activated too early to avoid that the context related data transferred to the new access router expire; (ii) ensure that the context related data are transferred only to the new access router that will hand off the connection. Such a framework is implemented by a layer-2 trigger that is based on three intelligent components, which are described in Sections 3.2, 3.3 and 3.4, respectively: (1) an estimator of the user movement, that allows to forecast the handoff time and the new access router selected to manage the handoff; (2) a ping-pong effect avoidance algorithm, that allows to avoid the undesired repetition of the handoff when a node moves around two or more cell borders; (3) a model of the CTP, to determine at run time the time needed to complete the context transfer, thus to activate the related procedure enough before the handoff starts. In this paper, we focus on the design of the trigger components, without taking into account aspects related to the trigger implementation, such as the CPU consumption and the power usage required for the additional forecasting task. A detailed description of the trigger architecture is given in the companion paper [4]. The following description of the node mobility and the estimation algorithm are based on standard methods of control systems [10, 15, 12, 3].
3.1 Dynamic system description The instant in which the mobile node goes out from its cell depends on the node position, speed, and direction. The dynamic system we use is based on the models in [10, 15, 12] and provides the dynamic state vector X(t), which represents the mobile node’s position, speed, and acceleration. With Z(t) we indicate the measurement vector, which is in relationship with the user movement. A detailed description of our dynamic system can be found in the companion paper [4]. The dynamic system allows to define a measurement equation to learn about the mobile user’s movement. In existing cellular systems, the distance between the mobile and a known base station is inherent in the forward link RSSI (received signal strength indication) of a reachable base station. The RSSI pi from a given cell i can be modeled as [10]: pi = p0i − 10rlog(di ) + ξi
(1)
where p0i = p0 + gb + gm is a constant determined by the antenna power p0 , the base station’s antenna gain gb , and the mobile node’s antenna gain gm ; r is a slope index that is typically 2 for wide cell, and is 4 for microcell; ξi is the shadowing component, which is found to be a zero-mean Gaussian random variable with standard deviation 4-8 dB. Finally, di represents the distance between the mobile node and the base station of cell i, which can be further expressed in terms of the mobile node’s position (x(n), y(n)) at time n and the location of the base station (ai , bi ):
di =
(x(n) − ai )2 + (y(n) − bi )2 .
To locate a moving user in the two-dimensional domain, at least three independent distance measurements are needed [10]. The necessary data are available in GSM systems each 0.48 s. To form the observation vector Zn = [p1 , p2 , p3 ]T we select the three largest measurements, which correspond to the three nearest neighbor cells. Zn is nonlinearly related to the dynamic state Xn through a function h(Xn ) that can be derived from (1) and (2): Zn = h(Xn ) + ξn . Applying the linearization method in [3], the linearized measurement equation becomes Zn = HXn + ξn with H = ∂h ˆ n the optimal estimation of Xn . , being X ∂X
ˆn X=X
3.2 Estimation algorithm of the mobile node state To obtain the evolution of the mobile node state, we use recursively an optimal estimator. Using an optimal Kalman filter, as described in [3, 12], the adaptive state estimator becomes: ˆ n+1 = AX ˆ n + Kn+1 (Zn+1 − HAX ˆn) X
θ = g(Vn ) = tan−1
(2)
(3)
where Kn+1 is the standard Kalman gain matrix. The algorithm that allows to estimate the state of the mobile node by RSSI values consists in the resolution of a linear system and is detailed in [4]. The state estimator is embedded into an handoff estimation algorithm composed of two steps: 1. estimate the dynamic state of a moving user; 2. select the neighboring cell toward which the mobile node moves. Step (1) is trivial, using subsequent RSSI measurements and applying the state estimator defined by (3). Therefore, let us focus on step (2). We assume that time t¯ is needed to execute the message exchanges for fast handoff and all the handoff operations. Furthermore, we consider that is necessary to carry out the handoff operations when the signal strength received from the base station decreases to an established value that is, when the mobile node moves away from the base station to an established distance th. This latter value can be identified as the least distance between the base station and any cell side; alternatively, it can be indicated from upper layer protocols. Step (2) can be decomposed in the following sequence of operations: (i ) estimate the time t that the mobile node (with a specific speed and a specific position) requires to be at distance th from the base station; (ii ) check that the estimated time t is greater than t¯ (t > t¯); (iii ) select the new access router to which the mobile node must handover and start the context transfer procedure (if the estimation time is near to t¯). Operations (i)−(iii) depend on the trajectory of the moving user, defined as:
y(n) ˙ x(n) ˙
(4)
T ˙ y(n)] ˙ is the speed vector. Assuming where Vn = [x(n), that the speed varies only with small change, (4) can be linearized as follows:
with G =
∂g ∂V
θ ≈ g(Vˆn ) + G(∆V ) ˆn V =V
=
−y(n) ˙ x(n) ˙ [ x˙ 2 (n)+ , ] y˙ 2 (n) x˙ 2 (n)+y˙ 2 (n)
(5) and ∆V =
Vˆn+s − Vˆn , where ∆V is the speed variation between instants n and n + s with s ≥ 1, and Vˆn is the speed estimation at the instant n. Let us now provide a more detailed description of operations (i) − (iii). First (op. (i)), we calculate the direction of the line l which joins the mobile node to the base station, and the mobile node trajectory θ. These two elements serve to derive the component vt of the speed, which lies on l. This component denotes the speed at which the mobile node moves away from (or approaches to) the base station. If vt indicates a leaving, we calculate the acceleration component on l and the time t that the mobile node will spend to reach the distance th from the base station. If time t is less or equal than t¯ (op. (ii)), we select the new base station to which the mobile node can handover (op. (iii)), and start the fast handoff operations (e.g., requiring a PrRtAdv message by a RtSolPr message). When the time t¯ is over, the link with the new base station can be established. As soon as the mobile node is inside the coverage area of the new access router, we increase the th value for a certain time ts , to avoid needless repetition of handoff due to the ping-pong effect. This effect shows itself when the mobile node crosses the border of two cells (beyond the distance th), and it can cause an handoff needless repetition from a base station to another. However, increasing the threshold th is only the first easy precaution to avoid the ping-pong effect. As discussed in the next section, to resolve this issue we have devised an efficient algorithm that estimates the next cross cell.
3.3 Ping-pong avoidance algorithm The handoff occurs because the mobile node moves away from the base station to which it is currently connected. This also means that the mobile node approaches to one or two new access routers. Therefore, besides the instantaneous position, the mobile node approaching and leaving time (to/from the neighboring base stations) are also important information. Our estimation system always refers to three base stations: the base station to which the mobile node is currently connected, and the two nearest neighboring base stations, denoted as Sa and Sb . There are three cases: 1. Sa is always closer and Sb is always further: this means that the mobile node is directed to Sa ; 2. Sa and Sb are both always closer: this means that the mobile node moves into an area that is very near to the cells border, and that the mobile node approaches both to Sa and Sb ; 3. Sa and Sb are both always further: this situation might not generally verify together with the handoff signaling, since it indicates that the mobile node is directed
inside the cell in which it is currently. Therefore, it is not further considered. If the ping-ping avoidance algorithm detects case (1), it estimates if the mobile node is really into the coverage area of Sa when the handoff operations terminate. For the estimate we suppose that, during the small time interval t¯, the mobile node can only make small changes in direction and speed that is, the trajectory is stable during t¯. If the estimate results true, the algorithm selects Sa as the new access router; otherwise, the algorithm increases th to the maximum allowed radius, and checks again for handoff. The motivation for the latter choice is that the mobile node is directed to Sa , but it is still too far to connect to Sa . If the handoff signaling remains but the estimate is false again, or if th has been just set to the maximum radius, it means that the mobile node moves near the union point of the three cells, where the cells have the least overlap, and where even small estimation errors cause the algorithm to predict that the mobile node is out of Sa coverage area rather than inside it. Under this condition, the algorithm selects Sa as the new access router (notice that Sb could be closer than Sa ) being the probability of the ping-pong effect greater than the probability that the mobile node unexpectedly changes its speed, acceleration, and direction, i.e., it changes its trend to approach to Sa . If the algorithm detects case (2) when handoff occurs, it estimates the position that the mobile node will have when the handoff operations are completed. If the estimate reveals that the mobile node will be into the coverage area of one base station (Sa or Sb ), this station is selected as the new access router. If the estimate reveals that the mobile node will be into an area covered by both the two cells, the algorithm selects as new access router the base station for which the mobile node has sensed the greatest signal strength. Finally, if the estimate finds that the mobile node will be out of the coverage area of both cells, the algorithm increases the th value and checks again for handoff. If either th has been just set to the maximum radius or the same conditions stand after the new check, the mobile node is evidently directed to the union point of the three cells. Therefore, it is possible to perform the handoff and the algorithm selects as new access router the base station for which the mobile node has sensed the greatest signal strength.
3.4 Performance model of CTP in a hierarchical architecture The layer-2 trigger forecasts the time t¯ taken by the mobile node to leave the current cell. When t¯ is greater but close to the time needed to perform completely all the phases of the fast handoff, the system informs the network layer, which starts the context transfer procedure. In an hierarchical handoff architecture (Figure 1), it is not always necessary to execute a context exchange. Indeed, during its movement the mobile node can change the access point (AP), still remaining into the same mobile anchor point (MAP) domain. This means that the context transfer is not activated until the mobile node leaves the current MAP domain to join a new one. Figure 2 shows the message exchange for the MIPv6 fast handoff and CTP. When the mobile node receives the PrRtAdv message, it knows if it is going to change the MAP domain. In case of handoff to a new MAP domain, the context transfer is activated by sending a CTAR message.
internet
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Figure 1: Hierarchical handoff architecture. MN (on pAP)
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Registration with home agent and all corresponding nodes
Figure 2: Message flow of MIPv6 fast handoff and context transfer protocol in a hierarchical access network.
From Figure 2 it results that the trigger has to signal the handoff t¯r = T1 + T2 + T3 instants before the mobile node disconnects from the pAP (disconnection instant). T1 is the time needed by the mobile node to send the PrRtSol message to the previous MAP (pMAP) and to receive the PrRtAdv replay message. The PrRtAdv message contains information about the nAP (that will be useful to generate the nCOA) and possibly information about the new MAP (nMAP), used by the mobile node to know if it is going to enter a different MAP domain. T2 = tf bu − tctar is the time interval elapsed between the delivery of the CTAR message and the delivery of the FBU message. T2 is determined by the ISP, because it depends on the bandwidth among MAPs and between the MAP and the mobile node. The mobile node is not capable to measure T2 , which is taken as parameter and used to determine the activation instant of the FBU procedure. T3 represents the time elapsed between the delivery of the FBU message and the disconnection instant. During this period the operations that allow the mobile node to communicate (at IP level) in the new sub-network are executed. If T3 is properly set, the mobile node receives the FBack message when it is still connected to the pAP link, and it executes the operations for the new connection (with the nAP). The time elapsed between tctd , the instant in which the context of the mobile node is available on the nMAP (and therefore on the nAP), and tpkf low , the instant in which the first
∆Tavail = tf bu − tctar +
1 (S − sctd ) RT TAR + 2 bAR
(6)
In a hierarchical architecture, ∆Tavail can be also expressed as the time the nMAP needs to transfer the new context to all the nAPs that it manages, that is: 1 sctd + O . (7) RT TAR + 2 bAR To design a more robust system, we assume that the delivery of context data (CTD message) requires a double value with respect to that determined in (7). From (6) we can derive the value tf bu − tctar that is, the time interval between the start of CTP and the fast handoff signaling. Hence, the definition for the handoff time t¯r is: ∆Tavail =
t¯r =T1 + T2 + T3 with:
(8)
2(S + O) 2(S + O) + (9) bM N bAR 1 sctd − S (10) T2 =tf bu − tctar = ∆Tavail − RT TAR + 2 bAR 2(S + O) 4(S + O) T3 =RT TM N + 2RT TAR + + (11) bM N bAR From (18)-(20) it follows that t¯r is a function of the available bandwidth and therefore changes with the network load. To provide a quantitative example we suppose that RT TM N = 200ms, RT TAR = 100ms, O = 46bytes, and that the data context fills the Maximum Segment Size of the data link frame that is, sctd = 1500bytes. T1 =RT TM N + RT TAR +
0.87
b_MN=170 Kbps
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Handoff time (sec)
0.86 0.855 0.85 0.845 0.84 0.835 0.83 0.825 0.82 10
8
6 4 Bandwidth (Mbps)
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0
Figure 3: t¯r as a function of bAR . 2
b_AR=10 Mbps
1.8
Handoff time (sec)
packet for the mobile node arrives to the nAP, is denoted as ∆Tavail (see Figure 2). It provides a measure of the time available to complete the context transfer before the mobile node disconnects from the pAP and the packet flow with the nAP starts. In the following we identify the conditions that allow to satisfy ∆Tavail > 0. If the context transfer is initiated by the pAP, ∆Tavail > 0 is always satisfied [2]. On the other hand, in our scenario, where the context transfer is initiated by the mobile node, ∆Tavail > 0 holds if the FBU message is timely delayed or better if the right instant to initiate the context transfer (CTAR message) is estimated. The latter goal is addressed by the estimation algorithm proposed in Section 3.2. ∆Tavail is a function of the following parameters: the maximum size S of the messages exchanged to perform the fast handoff and the context transfer; the size sctd of the message containing context data; the total overhead O needed to send a message (O = Oudp + Oip + Of rame ); the roundtrip time RT TM N experienced by a mobile node exchanging packets into the wireless network; the round-trip time RT TAR experienced by an access router (or MAP) exchanging packets into the wired network; the effective bandwidth of the link from the mobile node to the access point bM N and the links among the access routers bAR (we assume that the link among APs and their MAP have the same capacity). In this paper, we suppose that the overhead O is constant and the fluctuations of the RT T are negligible. Analyzing the message exchange in Figure 2, and considering that ∆Tavail = tpkf low − tctd we can obtain:
1.6
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Figure 4: ¯tr as a function of bM N . Figure 3 shows the trend of t¯r when bM N = 170Kbps and bAR ranges from 10M bps to 1M bps, while Figure 4 shows the results obtained when bAR = 10M bps and bM N ranges from 170 to 9Kbps. The analytical results show that a bandwidth degradation implies an increase of t¯r . If the estimated handoff time t¯ is greater than t¯r (t¯ ≥ t¯r ), no problem arises; after that t¯r is spent, the system checks the mobile node position and, if the node is too far to perform the layer-2 handoff, the system waits until the mobile node reaches a better position. If t¯ < t¯r , the context data cannot be used to facilitate the handoff process and to guarantee the proper service level, because the connection is handed off before the context transfer is completed. In this case, combining (6) and (10) we have that the time effectively available for context transfer is −S r r = T2 + 12 RT TAR − sctd . ∆Tavail is proportional ∆Tavail bAR to T2 and to the bandwidth between the access routers. If r is also reduced. An example of this bAR decreases, ∆Tavail phenomenon is shown in Figure 5. If the bandwidth between the access routers decreases, the value of the real available time tends to decrease, while the available time that could be guaranteed tends to increase. We can conclude that, to avoid that bandwidth variations lead ∆Tavail to unacceptable values, we need to size t¯ by supposing the worse network conditions, in such a way as to r guarantee that ∆Tavail is always lower than ∆Tavail .
DT_avail (sec)
0.115 0.11 0.105 0.1 0.095 0.09 10
8
6
4
2
0
Bandwidth(Mbps)
Available time as a function of bAR r (∆Tavail =real DT avail; ∆Tavail =DT avail).
Figure 5:
4.
SIMULATIVE AND ANALYTICAL RESULTS
4.1 Performance evaluation of the estimation and ping-pong avoidance algorithms We evaluate the capability of the proposed estimation algorithm to follow/reproduce the mobile node trajectory in a cellular network having a cell radius of 2 Km. The speed and acceleration of the mobile node can change during the trajectory, and there is no constraint on the trajectory that the mobile node can follow in the two-dimensional space. The simulation parameters are taken from [10] (see also [4]). The experimental results have been obtained through a simulation model developed using the Octave tool [14]. Our simulation model relies on the mobile node movement Equations (see [4]) and on equation (1). We assume an ideal channel where the received power is exactly defined by Equation 1 that is, we do not consider possible errors due to estimating a node position starting from RSSI. Figure 6 compares the real trajectory that the mobile node follows with the trajectory that the estimation algorithm computes. In the figure, the points in the plan represent the access point locations, while the shaded line circles represent the coverage areas of the access points with which the mobile node is connected during its movement. The dashed curve shows the real trajectory, while the solid curve depicts the estimated trajectory. In these simulations, the initial values of the dynamic system are intentionally set with errors in order to highlight the insensitivity of the adaptive Kalman filter to the initial conditions. 20000
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cellular network with cell radius of 4 Km and found that the algorithm performance is insensible to the cell radius length. In the next set of experiments we analyze the performance of the ping-pong avoidance algorithm. The mobile node moves in a region covered by cells having a radius of 2 km, which represent an access network configuration more sensible to the ping-pong effect than a scenario with cell radius of 4 Km. In the experiments, we use two different trajectories which are denoted by (a) and (b) and are shown in Figures 7 and 9, respectively. Case (a) is the most complex trajectory because the mobile node moves towards many border lines of neighboring cells. This case stresses heavily the ping-pong avoidance algorithm. Case (b) refers to a user moving in a urban region, where speed and trajectory show frequent discontinuity. Figures 7 and 9 show the results obtained without applying the ping-pong avoidance algorithm. The tables on the graphs compare the Theoretical Access point Path (TAP) with the Actual Access point Path (AAP), which is the access point path effectively traversed by the mobile node. The comparison of the access point paths highlights that the ping-pong effect shows itself every time the mobile node crosses the border lines of the neighboring cells, and the system can perform useless handovers with the same cells. AAP
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Figure 7: Case (a) without the ping-pong avoidance algorithm.
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Figure 8: Case (a) with the ping-pong avoidance algorithm.
Figure 6: Real and estimated trajectories. In not reported experiments, we have also considered a
Figure 8 shows the result for case (a) when the ping-pong avoidance algorithm is applied. The ping-pong effect is to-
tally disappeared and the AAP and TAP shown in the table match, as the useless handoffs with B, E, H, and M have been eliminated. By analyzing the simulation data, we have observed that, when the mobile node is directed to the intersection point of border lines of two or more cells (e.g., the point at the intersection among cells A, B, and C) and the handoff is signaled, the system notices the approach to two cells and sets th to the maximum radius. When the handoff is signaled again, the mobile node is very close to the border line of the cell and therefore it becomes simple to predict the new access point. When the mobile node enters into a region covered by two cells (e.g., B and C), it connects indifferently to one of the two access routers, for example C. The estimate errors cause a lot of handoff signaling (for the access point of cell B), but the useless handovers are avoided by increasing th to the maximum radius. However, when the mobile node is very close to the new cell (e.g., cell D), the handoff becomes necessary: the system notices that the available access points are those in cells B and D. The system also notices that the mobile node shows an approach to cell D and a leaving from cell B, so the system selects as new access point that in cell D, although the access point in cell B is closer. AAP TAP 20000
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Figure 9: Case (b) without the ping-pong avoidance algorithm.
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Figure 10: Case (b) with the ping-pong avoidance algorithm. Figure 10 shows what happens when the ping-pong avoidance algorithm is applied to case (b). We can observe a reduction of the ping-pong effect, but in this case two useless handoffs remain: the handoff to the access point in cell
D and that to the access point in cell I. The motivation is that we consider the instant in which the mobile node is connected to the access point in cell C and is directed towards cell D. At a specific instant the system predicts that the mobile node, after t¯ seconds, will be beyond the th threshold. Furthermore, the system notices that the mobile node is always closer to D and further to F ; the system also predicts that, after the handoff, the mobile node will be in the coverage area of the access point in cell D. All these information instruct the system that the access point in cell D must be selected as new access point. However, when the handoff phase terminates, the mobile node changes suddenly its direction; it remains in cell C and this makes useless the handoff to cell D. We can conclude that the handoff to the access point in cell D is a comprehensible error, since the system is able to predict the mobile node behavior using only the physical input and the movement trend of the mobile node. Therefore, the system never foresees that the mobile node stops and goes back immediately after the handoff conclusion.
4.2 Analysis of the handoff time in a real scenario In this set of experiments, we analyze the handoff time in the GSM network architecture in which the General Packet Radio Service (GPRS) is used and the mobile IP protocol is MIPv6. In this environment, the GGSNs play the role of access routers and the maximum bandwidth of the radio link is around 170Kbps. However, this value decreases on the basis of the number of frame slots that the mobile node can use. The number of used slots depends on the mobile device class, coding type used, and the traffic in the cells involved in the communication [6]. In the worse hypothesis, the mobile node could be a class 1 device and it could use a CS-1 coding: this implies that the minimum allowed bandwidth is bmin M N = 9.05Kbps. This bandwidth can be also offered to mobile devices with different characteristics, when the high network load forces the GPRS communications to use the minimum number of frame slots. GSM network suffers from a high transmission delay for data transfer: this determines an average round-trip time that varies from few hundreds of milliseconds to one second [6]. In our analysis we use the typical value of RT TM N = 200ms. For the wired network (that connects the access routers and the MAPs) we suppose a maximum bandwidth bAR = 10M bps and a round-trip time RT TAR = 100ms. In this scenario, assuming that the context is contained in a full datagram (sctd = 1500bytes), we r when the available bandwidth becompute t¯r and ∆Tavail tween the access routers decreases from 10M bps to 1M bps. The corresponding results are reported in Figures 11 and 12. Figure 11 shows the trend of t¯r as a function of bAR . When bAR decreases to 1M bps, t¯ becomes around 2.03 secr augments when bAR deonds. Figure 12 shows how ∆Tavail creases. In this case, we also suppose (to oversize the triggering system) that the nMAP needs 2∆Tavail = 2 12 RT TAR + sctd +O seconds to distribute the context data to the acbAR cess router in its domain. As we see in Figure 12, when r becomes around 125ms. We bAR reaches 1M bps, ∆Tavail can conclude that, by setting the handoff activation time t¯ r will to a value equal or greater than 2.03 seconds, ∆Tavail be always greater than ∆Tavail . This result is shown in Figure 13, in which we have reported the trend of the real
2.035
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Acknowledgments
Figure 11: t¯r as a function of bAR . 0.125
This work is partially founded by the Italian MIUR in the framework of the FIRB project “Performance Evaluation of Complex Systems: Techniques, Methodologies and Tools”. We thank Alberto Lima that helped us to design the layer-2 trigger.
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6. REFERENCES
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Figure 13: Available time as a function of bAR . and estimated available time, when the available bandwidth decreases and t¯ is set to 2.04 seconds.
5.
protocol stack, that predicts the handoff time and, on the basis of a proposed CTP performance model, estimates the context transfer time taking into account the actual network conditions. We have shown that is possible to forecast the handoff time for simple and complex mobile node trajectories, and we have also proposed a ping-pong avoidance algorithm that allows to drastically reduce the number of handoffs in critical paths. We have also demonstrated that a proper setup of the context transfer activation time allows to transfer the context under different network conditions before the handoff procedure starts.
CONCLUDING REMARKS
To guarantee a proper QoS level is necessary to use updated information about the state of the user session, the mobile node and the access network characteristics, and about the state of the mobile node (physical location, network location, and node resources state). However, the Context Transfer Protocol is an effective support to the QoS management only if the system is capable to provide valid information at the access router that will hand off the ongoing session. In this paper, we have proposed a mechanism that enables mobile nodes to activate the context transfer procedure on the basis of their speed and trajectory and the local topology of the access network. The adopted mechanism is a layer-2 trigger, implementable as a module of the network
[1] N. Bartolini, P. Campegiani, E. Casalicchio, and S. Tucci. A performance study of context transfer protocol for qos support. In Proc. of 19th Int’l Symp. on Computer and Information Sciences, Kemer, Antalya, Turkey, 2004. [2] N. Bartolini and E. Casalicchio. A performance analysis of Context Transfer Protocols for QoS enabled Internet services. Computer Networks, 47, July 2005. [3] R. G. Brown and P. Y. C. Hwang. Introduction to Random Signals and Applied Kalman Filtering, 3rd Edition. J. Wiley, New York, 1996. [4] E. Casalicchio, V. Cardellini, and S. Tucci. Design and performance evaluation of mechanisms for mobile-devices handoff forecast. In Proc. of FIRB-Perf Workshop on Techniques, Methodologies and Tools for Perf. Eval. of Complex Sys., Torino, Italy, Sept., 2005. [5] S. Deering and R. Hinden. Internet Protocol, version 6 (IPv6) specification. RFC 2460, Dec. 1998. [6] H. Inamura, G. Montenegro, R. Ludwig, A. Gurtov, and F. Khafizov. TCP over second (2.5G) and third (3G) generation wireless networks. RFC 3481, Feb. 2003. [7] D. Johnson, C. Perkins, and J. Arkko. Mobility support in ipv6. RFC 3775, June 2004. [8] J. E. Kempf. Problem description: Reasons for performing context transfers between nodes in an IP access network. RFC 3374, Sept. 2002. [9] R. Koodli. Fast handovers for mobile IPv6. IETF Mobile IP Working Group Internet-Draft, Jan. 2004. [10] T. Liu, P. Bahl, and I. Chlamtac. Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE J. on Selected Areas in Communications, 16(6):922–936, Aug. 1998. [11] J. Loughney, M. Nakhjiri, C. Perkins, and R. Koodli. Context transfer protocol. IETF Seamoby WG Internet-Draft, Aug. 2004. [12] R. L. Moose and H. F. Vanlandingham. Modeling and estimation for tracking maneuvering targets. IEEE Trans. Aerosp. Electron. Syst., 15, May 1979. [13] T. Nartel, E. Nordmark, and W. Simpson. Neighbor discovery for IP version 6 (IPv6). RFC 2461, Dec. 1998. [14] Octave, 1998. http://www.octave.org/. [15] R. A. Singer. Estimating optical tracking filter performance for manned maneuvering targets. IEEE Trans. Aerosp. Electron. Syst., 1970. [16] S. Thomson and T. Narten. IPv6 stateless address autoconfiguration. RFC 2462, Dec. 1998.
