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Preemption-Aware Instantaneous Request Call Routing for Networks With Book-Ahead Reservation Iftekhar Ahmad and Joarder Kamruzzaman
Abstract—This paper presents a new preemption-aware quality of service (QoS) routing algorithm for instantaneous request (IR) call connections in a QoS-enabled network where resources are shared between IR and book-ahead (BA) call connections. BA reservation, which confirms the availability of resources in advance, is a highly attractive technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns for the implementation of BA reservation is the need for preemption of on-going IR calls to accommodate BA calls when resource scarcity arises. Preemption disrupts service continuity of on-going calls which is considered as severely detrimental from users’ perceived QoS definition found in recent studies. Existing QoS routing algorithms focus on resource conservation or load balancing as the key objective to attain in addition to guaranteed QoS. No works have yet focused on the preemption problem of on-going IR calls at routing stage in the presence of BA calls. We present a mathematical formulation to compute the preemption probability of an incoming IR call at routing stage based on the current IR and future BA load information. We propose a routing strategy by formulating a link cost function comprising of the calculated preemption probability of the incoming IR call and hop count. Simulation results confirm that QoS routing based on the proposed link cost function significantly outperforms widely recommended shortest path and widest path routing algorithms in terms of IR call preemption and blocking rate. The proposed approach also yields higher network utilization and IR effective throughput. Index Terms—Multimedia applications, quality of service, resource reservation, routing.
I. INTRODUCTION NCREASING use of high-speed multimedia and distributed applications has made quality of service (QoS) consideration an important issue for both wireline and wireless networks. Resource reservation is one of the key techniques used to facilitate guaranteed QoS. In general, two types of reservation techniques have been proposed by researchers: 1) book-ahead (BA) reservation and 2) instantaneous request (IR) reservation. Applications like multiparty video conferencing, video on demand, live broadcast of TV programs, telemedicine, tele-teaching, grid computing, distributed simulations, etc. which carry time-specific significance and require high bandwidth demand are good candidates for BA reservations [1]–[9]. A BA call connection
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Manuscript received May 28, 2006; revised March 28, 2007. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Qian Zhang. The authors are with the Faculty of Information Technology, Gippsland School of Computing and Information Technology, Monash University, Victoria 3842, Australia (e-mail:
[email protected],; Joarder.Kamruzzaman@ infotech.monash.edu.au). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMM.2007.906560
requests for resource reservation in advance to enjoy confirmed availability of resources from its start over the complete lifetime. Contrarily, an IR call connection requests for immediate reservation and usage of resources [1]. One of the problems with BA reservation is the requirement for preemption of on-going IR calls to supply the required resources to a BA call if resource scarcity arises at its activation point. Rerouting of preempted IR calls is often thought as a solution to maintain service continuity in a small low-speed network. However, rerouting is costly in a large network because of the length of time needed to find and establish an alternative path [22]. Also the “no rerouting” restriction applies for high speed networks where the bandwidthdelay product far exceeds the buffer capacity of the network [12]. This makes preemption a severe threat to service continuity of on-going IR calls which users perceive as a very important QoS issue [10], [11]. Maintaining a low preemption rate is thus considered as a pressing need in a QoS-enabled network. Existing works in literature with the aim to achieve low IR call preemption rate suggest strategies mainly at IR call admission control (CAC) stage. In [1], Schelen and Pink introduced the concept of look-ahead time at the CAC stage to address the issue of high preemption rate. Look-ahead time was defined as the pre-allocation time, i.e., the time for starting to set aside resources for BA reservation so that there is no resource scarcity at the activation time of a BA call. Authors in [1] used constant look-ahead time to reduce preemption rate. Ahmad et al. [3] showed that a dynamic measurement of look-ahead time is a better option compared to constant look-ahead time as networks are essentially of dynamic nature. In [5], Degermark et al. assumed that all calls, whether booked in advance or immediate, declare their duration in advance at the CAC stage. Quantitatively, IR calls are expected to dominate a commercial network and hence it is not reasonable to impose that the duration of an IR call be specified in advance which is often context dependent and unforeseeable. As discussed and also evident in other related works [6]–[8], almost all the works in literature attempted to bring modification at the CAC stage to achieve lower preemption rate. However, CAC is the second stage of a call setup procedure that is used to validate the feasibility of accepting a call. QoS routing is the first stage of the procedure to select a qualified path and then CAC is applied along the path. Like all other major network performance metrics, the preemption rate is influenced by the routing algorithms. However, no works known to the authors have yet addressed QoS routing to achieve a low preemption rate in a network where activation of BA calls is a major cause for preemption of on-going IR calls. Under such circumstances, there is a need to devise a new routing strategy that takes resource sharing between IR and BA calls into account to achieve a low-IR call preemption rate.
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AHMAD AND KAMRUZZAMAN: PREEMPTION-AWARE IR CALL ROUTING FOR NETWORKS WITH BA RESERVATION
In general, a routing problem is formulated as a path cost optimization problem which optimizes the value of objective function over the links connecting source to destination. The objective function is typically formulated as a function of number of hops, available bandwidth, delay, interference level, etc. Algorithms like Dijkstra’s or Bellman-Ford are used to find the optimized path [13]. For QoS routing, constraints in the form of QoS parameters are placed in the optimization problem. There are mainly two categories of constraints: 1) path constraints and 2) link constraints. Path constraints are placed over the complete path (e.g., end to end delay) while link constraints are applied at individual links (e.g., bandwidth request). The problem of pathconstrained cost optimization is NP-complete and computationally expensive while link-constrained cost optimization problem is tractable and less expensive [14]. In general, the path-constrained shortest path problem is solved by heuristics and approximate solutions. Another way to solve the path-constrained shortest path problem is to convert the path-constraint metric to the link-constraint metric and solve the link-constrained routing [16]. For cost optimization routing, two common approaches to the choice of link cost are: resource conserving and load distributing. Researchers in favor of resource conservation support that algorithms with a strong bias for minimum-hop routes (e.g., shortest path routing) almost always outperform other routing algorithms that do not consider path length [18]–[20]. Another group recommends load distributing approach (e.g., widest path routing) as the better choice because it has the advantage of making good use of network resources over a resource conserving approach [12], [21]. Almost all the routing algorithms proposed in literature with the exception like [9] considered the same approach for IR and BA call routing. However, privilege levels of BA and IR calls are not the same and, therefore, treating both types of connection requests through the same routing approach degrades network performances. Since a BA call requests for resource reservation well in advance, IR calls are given less privilege over BA calls and preempted to support BA calls if required. Routing an IR call through a loaded link at which a good number of BA calls are waiting for activation in near-future places that IR call at a higher risk of preemption. Avoiding such a loaded link is a good option to reduce the preemption probability of an IR call. In this paper, we propose an IR routing algorithm that takes the preemption probability for an IR call at each link into consideration. A mathematical derivation to calculate the preemption probability of an incoming IR call at a link in the presence of BA load information is presented. A link cost function is then formulated with a preference to avoid links that show higher preemption probability. Simulation results show that the proposed routing algorithm achieves significant improvement in terms of IR preemption and call blocking rate compared to shortest path and widest path routing algorithms.
II. BA RESERVATION AND PREEMPTION OF IR CALLS A BA call request needs to announce its starting time and call duration along with other QoS parameters, e.g., end to end delay, jitter, packet loss rate, bandwidth, etc. A routing algorithm then searches for a path from source to destination that will support
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TABLE I A BOOK-AHEAD TABLE
Fig. 1. Preemption scenario of IR calls.
the required QoS from the stated starting time for the declared duration [9]. The CAC algorithm at each node along the path checks whether there will be enough resources for that BA call for the announced duration from the declared starting time. If CAC finds it feasible, it allocates the resources for the whole announced duration and reflects the changes in available resources in the BA table. A BA table (Table I) is maintained to ensure that over-allocation of resources is always avoided. Arrival or departure of any BA call is reflected in the aggregate in the book-ahead table. For better resource manBA load agement, a minimum book-ahead time for BA reservation is imposed. Book-ahead time is the time interval between making a BA reservation request and its starting time. A minimum bookahead time ensures lower call-blocking probability for BA calls and better planning for IR calls. Resource management technique like look-ahead time based CAC requires that the minimum book-ahead time be reasonably long, at least greater than the look-ahead period [3]. For any current time and minimum book-ahead time , the aggregate BA load information at each BA activation point within the interval does not change in the BA table in the event of a new arrival or departure of BA calls. Fig. 1 shows a scenario where BA calls result in preemption of IR calls upon activation. Resource scarcity is evident at the starting point of BA call 2 and 3. Preemption of some IR calls is required to make room for these BA calls. Although a number of works have been reported in literature to reduce preemption rate by adopting several techniques at the CAC stage, no works known to the authors have aimed at reducing preemption rate at routing stage when resources are shared between IR and BA calls. Widely recommended routing algorithms like shortest path or widest path routing do not address this problem of on-going IR call preemption for a guaranteed communication network where resources are shared between IR and BA calls.
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III. EXISTING ROUTING ALGORITHMS AND THEIR LIMITATIONS Shortest path routing algorithm is widely recommended for a commercial network because of its resource conservative nature [18]–[20]. For a BA call connection request in a network , bandwidth-constrained shortest path topology routing problem is stated as follows. Given a BA connection request , with source , destination , bandwidth requirement , starting time and duration , find a path that supports the bandwidth requirement during and has minimal cost , where the interval (1) Here, is a path comprising of links that connects to and is the link cost function of individual link ( for shortest path routing). is the set of nodes and is the set of links in the topology. The bandwidth constraint of the routing problem is solved by pruning the links that do not have enough resources available for BA reservation for the declared interval. Additional QoS constraints like delay or packet-loss rate are often converted to a single bandwidth constraint to minimize the complexity of the QoS routing task [16], [17]. Dijkstra’s algorithm is then applied to the qualifying links in the topology to find the least-cost path. For an IR call connection request with source , destination , and bandwidth requirement , the problem of finding a bandwidth-constrained shortest path is similar to that of BA routing with the variation that available bandwidth at current time is considered for link pruning in IR routing while for BA routing bandwidth available at future time interval (as listed in BA table) is considered. Widest path routing considers traffic load in the link cost function and routes a call connection through the minimum loaded links. Widest path routing is achieved by the use of path cost metric expressed is inas a convex function of link cost metrics , where versely proportional to the available bandwidth at link [15]. In addition to shortest path and widest path routing, there is another technology of routing known as the minimum interference routing algorithm (MIRA) [17], [29], [30], where the basic idea is to select a bandwidth guaranteed path as to ensure that the selected path does not contribute to the blocking of future incoming calls. At the routing stage none of the above mentioned routing strategies considers the probability of an IR call to be preempted due to the activation of future BA calls. Strategy at the routing stage to keep preemption rate low should include searching for a path from source to destination with sufficient available bandwidth and minimum preemption probability over the links connecting the path. A model is required to calculate the preemption probability of an in-coming IR call at a link based on the current IR and future BA load information. In Section IV, we propose a mathematical formulation to calculate the preemption probability of an IR call for routing decision. This preemption probability in addition to hop count is then used to formulate a cost function which when optimized provides a routing decision that combines the merit of resource conservation and probabilistic estimation of IR call preemption utilizing a priori knowledge of BA resource reservation. Among the existing choices of link cost we select hop count as the preferred
choice as IETF [18] recommends shortest path as the preferred routing approach for a commercial network. IV. PROPOSED PREEMPTION-AWARE (PA) ROUTING FOR IR CALL CONNECTIONS Shortest path IR routing depends solely on the current state of network information even when a partial knowledge of future state information in the form of BA load is available. Each BA table associated with each individual link contains information about future activation time, duration and bandwidth demand of BA calls which remain unchanged for a length of minimum book-ahead period with respect to current time. This future load information in addition to the current network state information forms the basis of our calculation of preemption probability of an incoming IR call connection at the routing stage. Upon arrival of an IR call at time , chance of its preemption at a future BA activation time depends on three key factors: 1) whether the IR call will continue beyond ; 2) whether activation of BA call(s) at will cause resource scarcity and the IR call will be selected for preemption to meet the scarcity; and 3) whether the IR call will not face preemption at any point prior to . The probability that the IR call will be preempted is thus a product of the probabilities of the above three key occurrences. A. IR Call-Preemption Probability Let us consider an IR call request arriving at time . We of call connection at define the preemption probability a link due to activation of BA call(s) at time as [4] (2) Here, is its duration, is the probability that the call duration will extend beyond , is an estimate of the chance of preemption subject to resource scarcity, is the probability of connection continuity (explained and is not a delater) of the IR call till time . Call duration clared value for IR calls, but it follows a certain distribution in a practical network. Exponential distribution is widely recommended to model the lifetime of call connection in a practical network [3], [6]. Assuming an exponential distribution of life(mean duration of IR calls in the nettime with a mean of is expressed as work), (3) The term indicates the probability of call being selected for preemption in case of resource scarcity. Resource scarcity is estimated based on the current IR and future BA load information. Once resource scarcity is experienced at time , the probability of that call being selected for preemption depends on the magnitude of scarcity and the policy to select which call(s) to as preempt. We formulate (4) Here, is the normalized amount of resources that needs to be preempted from IR calls to accommodate the BA call(s) at time and is the weight that dictates the preference
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AHMAD AND KAMRUZZAMAN: PREEMPTION-AWARE IR CALL ROUTING FOR NETWORKS WITH BA RESERVATION
of the preemption policy to select IR call is expressed as
for preemption.
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that the call survives the resource scarcity occurred at earlier in (2) indicates the probability points in time. The term of connection continuity till time and is computed as (5)
(7)
is the amount of bandwidth used by all IR calls at Here, is the aggregate bandwidth required for current time , BA calls at time , is the bandwidth demand of connection and is the link capacity. The term when positive indicates the amount of resource scarcity and indicates the IR load at time estimated conservatively The conservative based on the current information at time estimation assumes that the amount of resources from releasing . calls will be consumed by the new calls in the interval Practical preemption policies suggest that, at a given level of , the same call may be subject to higher or resource scarcity lower chance of preemption depending on preemption policy. For example, a call with lower priority has higher chance of preemption in a priority based preemption policy whereas it may have lower chance of preemption if calls are preempted in last-in-first-out (LIFO) fashion in a LIFO-based preemption policy. However, when the scarcity is very low or very high, the difference of preemption probability among different IR calls is marginal irrespective of the preemption policy. For example, at which indicates that all the IR calls are rea point with equals quired to be preempted, the chance of preemption 1 for all IR calls. A model to calculate the preemption probability for an IR call should, therefore, take resource scarcity and the priority preference governed by the preemption policy into consideration. We propose the following polynomial model to : compute
. Considering Since the IR call starts from time , number of subsequent BA call activation times ; till point (i.e., probability of connection continuity the call survived beyond ) is computed as
(8) indicates the preemption probability due to actiwhere vation of BA call(s) at time . The preemption probability for call connection at a link over potential scarcity (BA activation) points is then expressed as
(9) If a large number of BA calls are requested in advance the value of is also likely to be large. Considering time complexity, the needs to be restricted and it can be set such that choice of , where is a non-negative integer set by the network provider. B. Modeling Preemption Policy
(6) For policy-invariant routing where the routing algorithm does not consider the type of preemption policy in use, is set to 1 for all IR calls. Policy-invariant routing can be more preferred a routing approach where modeling of is computationally expensive for the preemption policy in use. For policy sensitive routing that incorporates the preference of the policy to preempt the incoming IR call, the value of depends on the type of that IR call. Again, the value of varies with different preemption policies and should reflect the distinction between different IR calls. In Section IV-B, we show two examples of how to model for two different preemption policies. For expression (6), we choose polynomial modeling as it produces better performance for polynomial modeling compared to other modeling like exponential or linear as supported by simulation results. The product of the first two terms in (2) indicates the probability of preemption for call connection at a potential scarcity point when the call is not preempted at any point before . In practice, there can be many potential scarcity points (BA activation times) during the lifetime of an IR call. An IR call arriving if it is preempted at any at time will not continue to time other point earlier than . The probability of connection continuity beyond a certain point in time depends on the probability
A preemption policy determines which call(s) to preempt to accommodate higher priority calls upon resource scarcity. Preemption policy like LIFO prefers calls with relatively longer duration in the network to continue and selects calls in LIFO fashion for preemption [2], [6]. The philosophy behind LIFO policy is that the amount of traffic transferred by incomplete calls (defined as wasted revenue by Greenberg et al. [2]) needs to be minimized to maximize revenue return. This follows the standard definition of a guaranteed QoS call connection which is logically regarded as a complete transaction [28]. Interruption of the assured service carries little meaning for guaranteed performance communication making its revenue return insignificant. A resource reservation protocol like MPLS-TE [25] uses priority based preemption policy which selects IR calls from lower to higher priority when resource preemption is required. Higher priority call connections that generally represent important applications are likely to yield higher revenue return and hence enjoy better service compared to lower priority call connections. In the following, we show how the weight in (4) is formulated to model LIFO and priority-based preemption policies. For a LIFO policy, at a given scarcity level , the probability for an IR call to be selected for preemption is proportionate to the difference between the starting time of an IR call and the activation time of a nearby BA call. As a result, for an IR call
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arriving at time and having impending BA call(s) to be activated at , the value of in LIFO can be determined as follows
(10) Expression (10) ensures that the closer the IR call is to the potential point of resource scarcity, the higher is the probability of preemption. For a priority-based preemption policy, the value of for an IR call at a given scarcity level s does not depend solely on its priority level. It also depends on the number of on-going lower priority preemptable calls in the link. If there exists very small number of lower priority calls in the link, the probability for preemption is higher even for a higher priority IR call. For an incoming IR call with priority level (priority is expressed in ascending order with 0 being the lowest priority level), the value of is can be modeled as
Fig. 2. Simulation topology.
The proposed routing algorithm can be used in conjunction with look-ahead time based improved CAC scheme like dynamic look-ahead time (DLAT) model that reduces preemption rate at the CAC stage [3]. The look-ahead time in DLAT model is calculated by the following equation:
(11) indicates the number of IR calls with priority level Here, in the link. indicates the ratio of the number of lower priority calls to the number of calls with the same priority level as the incoming call. Expression (11) assumes that IR calls with the same priority level are preempted in LIFO order among themselves to minimize wasted revenue.
C. Preemption-Aware (PA) Routing Algorithm We propose to incorporate the preemption probability calculated above as an additional metric to the link cost function of an existing routing algorithm. Denoting the preemption prob, the proposed new cost ability at link calculated in (9) as function for link is defined as (12) The coefficient “ ” is used to set the level of emphasis on preemption probability in the link cost function. Considering the recommendation of IETF [18] to exercise resource conservation in a commercial QoS-enabled network, a hop (link) count metric is taken in this work as the preferred choice of existing ). The outline of the proposed PA routing link cost (i.e., algorithm is given as follows. Step 1: For an IR request requiring bandwidth demand , find from by pruning the links that have available bandwidth less than . Step 2: Compute link cost .
for each link in
, where
Step 3: Apply Dijkstra’s algorithm to find the least-cost path starting from source node to destination node and return path .
(13) Here, is the look-ahead time w.r.t. traffic condition at current time and BA activation time . , have the usual meaning defined earlier, is the mean is the mean arrival rate of bandwidth demand of IR calls, IR calls, is the normalized BA limit which sets maximum allowable aggregate BA load w.r.t. total link capacity, is the is the standard devicall-blocking probability for IR calls, is a tuning parameter. According to the DLAT ation and model, a call is admitted when the following rule is satisfied: (14)
V. PERFORMANCE ANALYSIS A. Simulation Setup Fig. 2 shows the network topology used for the simulation. The topology represents a typical ISP network that follows the ATT backbone network structure and has been simulated in various studies [4], [26], [27], [29], [30]. The capacity of each link is considered as 20 Mbps and call connections enter or leave the network through the gateway nodes. Bandwidth demand of each BA call is uniformly distributed in the range of 1.0 to 2.0 Mbps and that of each IR call is uniformly distributed in the range of 64 to 256 kbps. Lifetime of BA and IR calls is exponentially distributed with a mean of 300 s and 120 s, respectively. Arrival of BA and IR calls is assumed to follow a Poisson distribution with a mean arrival interval of 10 s and 200 ms, respectively. Simulation was also conducted for other mean values of connection load and lifetime and the results were found to be consistent with the results reported in this section. Since the study is based on BA reservation, each simulation is repeated for different BA limit
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AHMAD AND KAMRUZZAMAN: PREEMPTION-AWARE IR CALL ROUTING FOR NETWORKS WITH BA RESERVATION
Fig. 3. IR call preemption rate with SP, PMIRA, and policy-invariant PA routing for LIFO-based preemption policy at 95% confidence interval.
which sets the normalized limit on link capacity that the aggregate BA load can use so that starvation for IR load is avoided. We investigated a number of key network performance metrics: 1) IR call preemption rate; 2) IR call blocking rate; 3) network utilization; and 4) IR effective throughput to compare the performance of the proposed preemption-aware (PA) routing algorithm against the standard shortest path (SP), widest path (WP), and the precomputation scheme for minimum interference routing algorithm (PMIRA) [30]. It can be noted that among the existing minimum interference routing algorithms, PMIRA scheme is known to be the most effective scheme in terms of network performance measures [30]. IR call preemption rate is defined as the ratio of the number of preempted IR calls to the number of admitted IR calls in the network. IR effective throughput is the amount of traffic transferred by the complete sessions of IR calls. Other metrics follow their standard definitions. Experiments were conducted for both prein (4)] and preemption policy emption policy-invariant [ sensitive routing with LIFO and a priority-based preemption policy in use ( is determined by (10) and (11), respectively). For priority-based preemption policy, we considered three priority levels: 0 (lowest), 1 (medium) and 2 (highest). For call admission control DLAT based CAC model [3] was used. We used a modified version of ANCLES simulator [23] to conduct the simulation. B. Preemption Policy-Invariant PA Routing In this section, we present the performance of the proposed preemption policy-invariant PA routing against PMIRA, SP, and WP routing. For policy-invariant PA routing, the preemption policy that governs the selection process of on-going IR call(s) for preemption to meet resource scarcity due to activation of BA call(s), has no influence on routing decision. We present performance analysis for two preemption policies: 1) LIFO and 2) priority-based preemption policy. 1) Last in First Out (LIFO) Policy: LIFO policy preempts IR call in order of their arrival time in the system. The call that has arrived most recently is selected for preemption in LIFO policy. Fig. 3 shows the achieved level of preemption rate in different routing algorithms. Results indicate that the proposed PA routing algorithm achieves the lowest level of preemption rate
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Fig. 4. IR call blocking rate with SP, PMIRA, and policy-invariant PA routing for a LIFO-based preemption policy at 95% confidence interval.
compared to PMIRA and SP routing. This validates the benefit of incorporating the information in the form of preemption probability into link cost function in PA routing. PA routing outperforms PMIRA and SP routing by a margin up to 3% and 7%, respectively, across different BA limits. Widest path routing was found to yield the highest preemption rate for all the BA limits as it consumes higher amount of resources making resource scarcity highly probable. IR call preemption rate increases with increasing BA limit as increasing number of BA calls admitted with higher BA limit requires more IR calls to preempt in order to supply bandwidth for activated BA calls. Fig. 4 shows that the proposed PA routing achieves the lowest call blocking rate compared to others. Although shortest path routing is aware of resource saving, it does not take the future load and congestion level in links into consideration. Preference to always follow the shortest path ignores the impact of current routing decision on future incoming IR calls. This is known as the interference problem for routing algorithms [16]. The PMIRA scheme is designed to address this problem and the effectiveness of PMIRA scheme is evident in the figure which indicates that PMIRA scheme achieves lower call blocking rate compared to SP routing. The PMIRA scheme, however, makes the routing decision based on current network load information and does not take the issues like future BA load and the lifetime of the incoming IR call connection into consideration. The proposed PA routing addresses the interference problem by taking the future level of congestion into consideration and this results in fewer critical links, which ultimately enables the proposed PA routing to achieve lower call blocking rate. WP routing yields the highest call blocking rate across all BA limits as it is not focused on resource saving. In terms of resource utilization, WP routing performs the best compared to others. This is because WP routing consumes higher amount of resources to balance the load in the network. PA routing attains better utilization than SP routing as it accepts more calls and sometimes traverses higher number of lightly loaded links compared to SP routing to avoid congestion. Fig. 5 indicates that the proposed PA routing achieves better resource utilization compared to PMIRA and SP routing scheme. The lower preemption rate has a twofold advantages for the network enterprise. Firstly, it causes less disruption in service
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Fig. 5. Network utilization with SP, PMIRA, and policy-invariant PA routing for a LIFO-based preemption policy. Fig. 7. IR call-preemption and blocking rate with policy (LIFO)-invariant PA routing for different at BA limit 0.7.
Fig. 6. IR effective throughput with SP, PMIRA, and policy-invariant PA routing for LIFO-based preemption policy. Fig. 8. IR call preemption rate with preemption policy (LIFO)-invariant PA routing for different flooding gaps at BA limit 0.7.
continuity of on-going IR calls and attains higher user satisfaction. Lewis et al. [24] showed that user dissatisfaction can have severe impact on a company’s long term financial future. A lower number of preempted IR calls achieved through the proposed PA routing results in lower number of dissatisfied users which stands for better financial prospect for the network enterprise in future. Secondly, traffic transferred by the preempted calls does not always yield full benefit for the network enterprise in terms of revenue return. This is depicted in Fig. 6, which suggests that lower level of preemption rate has clear advantages in terms of IR effective throughput. Since PA routing achieves lower IR preemption and call blocking rate, higher number of IR calls can complete their full sessions which increases the net IR effective throughput. PA routing produces up to 4% and 6% more effective throughput compared to PMIRA and SP routing, respectively. With BA revenue remaining the same for all the routing algorithms, an increase in IR effective throughput in conjunction with higher user satisfaction resulted from lower call-preemption and the blocking rate presents better economic prospect for the network enterprise. Results shown for PA routing so far are for equal emphasis on hop count and preemption probability in the link cost funcin (12)]. Fig. 7 shows the preemption rate for diftion [ ferent values of at . It is evident that increasing values
of “ ” result in decreasing level of preemption rate. This provides an opportunity for the network provider to tune “ ” and achieve the desired level of preemption rate. With an increasing value of “ ”, the call blocking rate tends to increase slightly. The impact of imprecise link state information resulted from periodically advertised state information was also investigated. The preemption rate in all routing algorithms decreases with ), where the term increasing flooding gap (Fig. 8 for “flooding gap” indicates the time interval used for updating the routing information. Increasing impreciseness of state information causes more call blocking, which consequently results in less resource scarcity and lower preemption. Fig. 8 illustrates that PA routing achieves the lowest level of preemption rate for all flooding gaps. Observations also suggest that PA routing achieves the lowest call blocking rate for all flooding gaps. Rein sults shown for PA routing in this section are for (Section IV-A) motivated by the observation that, for this particular simulation setup, a further increase in does not make a noticeable difference in the network performance. 2) Priority Based Preemption Policy: Priority based preemption policy preempts IR calls from lower to higher priority
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AHMAD AND KAMRUZZAMAN: PREEMPTION-AWARE IR CALL ROUTING FOR NETWORKS WITH BA RESERVATION
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Fig. 9. IR call preemption rate with SP, PMIRA, and policy-invariant PA routing for priority-based preemption policy.
Fig. 11. Network utilization with SP, PMIRA, and policy-invariant PA routing for priority-based preemption policy.
Fig. 10. IR call blocking rate with SP, PMIRA, and policy-invariant PA routing for priority-based preemption policy.
Fig. 12. IR effective throughput with SP, PMIRA, and policy-invariant PA routing for priority-based preemption policy.
levels upon resource scarcity. Figs. 9–12 show the comparative analysis for different routing algorithms for priority based preemption policy. Figs. 9 and 10 indicate that PA routing scheme maintains its better perfromance compared to PMIRA and SP routing in terms of IR preemption and call blocking rate. For overall resource utilization, PA routing outperforms PMIRA and SP routing (Fig. 11). Although PA routing outperfoms both PMIRA and SP routing comfortably in terms of IR effective throughput, relative gain is reduced when compared to LIFO policy (Fig. 12 versus Fig. 6). This upholds the philosophy of the LIFO policy, although higher revenue return from higher priority calls may yield better revenue earning in priority-based preemption policy. Further observations suggest that performances of PA routing in priority based policy with changing flooding gap and co-efficient values “ ” follow the same trend as in LIFO policy. C. Preemption Policy-Sensitive PA Routing Preemption-aware routing can incorporate the preemption policy to be accounted for in its routing decision. In Section IV, we presented the modeling of for two diffferent preemption policies; LIFO and priority-based policies. In this section, we assessed the performances of the policy-sensitive PA (PSPA) routing for LIFO and priority-based preemption policies. 1) LIFO Policy: IR preemption rate achieved in different routing algorithms is shown in Fig. 13. It reveals that PSPA
Fig. 13. IR call preemption rate with PMIRA, policy-invariant PA, and PSPA routing for LIFO-based preemption policy.
routing further reduces IR preemption rate compared to policyinvariant PA routing. With PSPA routing, an IR call that arrives at a link at a time near to the point of estimated resource scarcity is in high risk of preemption according to the LIFO policy. PSPA routing attempts to avoid selecting such links for routing of new IR calls which effectively results in saving of realeased bandwidth from being consumed by new IR calls prior to the point of resource scarcity. This causes the overall preemption rate to , PSPA routing achieves more than decrease. For BA
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TABLE II NUMBER OF PREEMPTED CALLS WITH PRIORITY-BASED PREEMPTION POLICY
Fig. 14. IR call blocking rate with PMIRA, policy-invariant PA, and PSPA routing for LIFO-based preemption policy.
Fig. 15. Network utilization with PMIRA, policy-invariant PA, and PSPA routing for LIFO-based preemption policy.
Fig. 16. IR effective throughput with SP, WP, policy-invariant PA, and PSPA routing for LIFO-based preemption policy.
2% less preemption rate compared to PA routing. In terms of the call blocking rate and utilization, both PA and PSPA routing perform comparably (Figs. 14 and 15). In terms of IR effective throughput, PSPA routing maintains its better performance (Fig. 16). 2) Priority-Based Preemption Policy: Results found in our simulation suggest that when PA routing considers the prioritybased preemption policy for calculating preemption probability, performances in terms of preemption rate, call blocking rate,
and utilization improve marginally compared to policy-invariant PA routing. However, the core benefit of PSPA routing is presented in Table II. Results in Table II indicate that PSPA routing results in less preemption of higher priority calls compared to that in PA routing. For example, at a BA limit 0.7, PSPA routing results in 88 preemptions for the highest priority [P(2)] call connections while PA routing results in 139 preemptions for the same priority level. Both PSPA and PA routing outperform PMIRA routing as significantly fewer preemptions are required in PSPA and PA routing. Favoring the highest priority calls has the potential benefit of higher revenue return, and in this aspect, PSPA routing is highly attractive for the network enterprise. For further comparative analysis, we simulated different variations of shortest and widest path routing (e.g., widest shortest path, shortest widest path, k-shortest path). In all the cases, the proposed PA routing consistently showed improved performances over the variations of shortest and widest path routing. Results in this section is shown for the simulation study on an ISP topology that follows the well-known KL graph structure. To ensure that the simulation results found in this work are not restricted by the topology structure, we simulated the proposed and existing routing schemes in an ISP topology that follows the fully connected graph and random graph structures. The outcomes were consistent with the results presented in this paper. Since single-run simulation results are not always reliable, we conducted the simulation for multiple (ten) runs with various combinations of simulation parameters and performed the standard t-test to check the consistency of the outcomes. For all the cases, the results achieved by the proposed routing scheme consistently outperform the results in other schemes. For all the cases, the P values for the t-test were found to be in the range of to . VI. CONCLUSION In this paper, a new preemption-aware QoS routing algorithm is proposed for IR call connections in a network that supports book-ahead reservation. We adopted a new strategy to incorporate future BA and current IR load information to make a
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AHMAD AND KAMRUZZAMAN: PREEMPTION-AWARE IR CALL ROUTING FOR NETWORKS WITH BA RESERVATION
routing decision for IR calls. A mathematical derivation is presented to calculate the preemption probability of an incoming IR call at each link. This calculated preemption probability is then used as a metric to formulate a new link cost function for least cost routing. Simulation results confirm that the proposed QoS-constrained preemption-aware routing significantly outperforms shortest and widest path routing in terms of IR call preemption rate, call-blocking rate, and effective throughput. In this paper, we explored the IR routing aspect for performance improvement in a network. A properly designed BA routing in conjunction with the proposed preemption-aware routing is also expected to improve the network performance. Future works will address BA routing aspect for network performance gain. REFERENCES [1] O. Schelen and S. Pink, “Resource sharing in advance reservation agents,” J. High Speed Netw. (Special Issue on Multimedia Networking), vol. 7, no. 3–4, pp. 213–218, 1998. [2] A. G. Greenberg, R. Srikant, and W. Whitt, “Resource sharing for book-ahead and instantaneous-request calls,” IEEE/ACM Trans. Netw., vol. 7, no. 1, pp. 10–22, Feb. 1999. [3] I. Ahmad, J. Kamruzzaman, and S. Ashwathanarayaniah, “A dynamic approach to reduce preemption in book-ahead reservation in QoS-enabled networks,” Comput. Commun., vol. 29, no. 9, pp. 1443–1457, May 2006. [4] I. Ahmad, J. Kamruzzaman, and S. Aswathanarayaniah, “Preemption-aware routing for QoS-enabled networks,” in Proc. IEEE Global Telecommunications Conf. (GLOBECOM 2005), Nov.–Dec. 2005, pp. 715–720. [5] M. Degermark, T. Kohler, S. Pink, and O. Schelen, “Advance reservation for predictive service,” in Proc. NOSSDAV’95, Durham, NH, Apr. 1995. [6] Y. Lin, C. Chang, and Y. Hsu, “Bandwidth brokers of instantaneous and book-ahead requests for differentiated services networks,” ICICE Trans. Commun., vol. E85-B, no. 1, pp. 278–283, 2002. [7] D. Wischik and A. Greenberg, “Admission control for booking ahead shared resources,” in Proc. INFOCOM’98, 1998, pp. 873–882. [8] D. Ferrari, A. Gupta, and G. Ventre, “Distributed advance reservation of real-time connections,” in Proc. NOSSDAV, Durham, NH, 1995, pp. 15–26. [9] R. Guerin and A. Orda, “Networks with advance reservations: The routing perspective,” in Proc. IEEE INFOCOM, Mar. 2000, pp. 118–127. [10] W. C. Hardy, QoS measurement and Evaluation of Telecommunications Quality of Service. New York: Wiley, 2001, pp. 169–172. [11] M. Campanella, P. Chivalier, and N. Simar, Quality of Service Definition 2001 [Online]. Available: http://www.dante.net/sequin/QoS-defApr01.pdf [12] B. Awerbuch, Y. Azar, and S. Plotkin, “Throughput competitive online routing,” in Proc. 34th Ann. IEEE Symp. Foundations of Computer Science, Nov. 1993, pp. 32–40. [13] D. Bertsekas and R. Gollager, Data Networks. Upper Saddle River, NJ: Prentice-Hall, 1992. [14] W. Lee, M. Hluchyj, and P. Humblet, “Routing subject to quality of service constraints in integral communication networks,” Proc. IEEE Network 1995, vol. 9, no. 4, pp. 46–55, 1995.
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[15] K. Kowalik and M. Collier, “Should QoS routing algorithms prefer shortest paths?,” Proc. IEEE ICC’03, vol. 1, pp. 213–217, 2003. [16] K. Hendling, T. Losert, W. Huber, and M. Jadl, “Interference minimizing bandwidth guaranteed on-line routing algorithm for traffic engineering,” in Proc. IEEE ICON’04, Singapore, 2004, pp. 497–503. [17] K. Kar, M. Kodialam, and T. V. Lakshman, “Minimum interference routing of bandwidth guaranteed tunnels with MPLS traffic engineering applications,” IEEE J. Select. Areas Commun., vol. 18, no. 12, pp. 2566–2579, Dec. 2000. [18] G. Apostolopoulos, D. Williams, K. Kamat, and R. Guerin, “QoS Routing Mechanism and OSPF Extensions,” IEEE Trans. Netw., vol. 7, no. 3, pp. 350–364, Jun. 1999. [19] C. Pornavalai, G. Chakraborty, and N. Shiratori, “QoS based routing algorithm in integrated services packet networks,” in Proc. Int. Conf. Network Protocols, Atlanta, GA, 1997, pp. 167–175. [20] I. Shaikh, J. Rexford, and K. S. Shin, “Evaluating the impact of stale link state on quality of service routing,” IEEE/ACM Trans. Netw., vol. 9, no. 2, pp. 162–176, Apr. 2001. [21] A. Kamath, O. Palmon, and S. A. Plotkin, “Routing and admission control in general topology networks with poission arrivals,” in Proc. SODA: ACM-SIAM Symp. Discrete Algorithms, Atlanta, GA, 1996, pp. 269–278. [22] J. Oliveira, C. Scoglio, I. Akyildiz, and G. Uhl, “A new preemption policy for diffserv-aware traffic engineering to minimize rerouting,” Proc. IEEE INFOCOM 2002, vol. 2, pp. 695–704, Jun. 2002. [23] ANCLES 2006 [Online]. Available: http://www1.tlc.polito.it/ancles/, [24] S. Lewis, Measuring the Relationship Between Satisfaction and Spending, Articles in Velocity 2002 2006 [Online]. Available: http://development2.com/pdfs/velocity.pdf, [25] Vasseur, L. Chen, and C. Scoglio, LSP Preemption Policies for MPLS Traffic Engineering 2003 [Online]. Available: http://daft-deoliviera-diff-te-preemption-02.txt [26] G. Banerjee and D. Sidhu, “Comparative analysis of path computation techniques for MPLS traffic engineering,” Comput. Netw., vol. 40, no. 1, pp. 149–165, 2002. [27] M. Kodialam and T. Lakshman, “Dynamic routing of restorable bandwidth-guaranteed tunnels using aggregated network resource usage information,” IEEE/ACM Trans. Netw., vol. 11, no. 3, pp. 399–410, Jun. 2003. [28] S. Jha and M. Hassan, Engineering Internet QoS. London, U.K.: Artech House, 2002. [29] B. Wang, X. Su, and C. Chen, “A new bandwidth guaranteed routing algorithm for MPLS traffic engineering,” Proc. IEEE ICC02, pp. 1001–1005, 2002. [30] G. Retvari, J. J. Biro, T. Ciinkler, and T. Henk, “A precomputation scheme for minimum interference routing: The least-critical-path-first algorithm,” Proc. IEEE INFOCOM’05, pp. 260–268. Iftekhar Ahmad is a postdoctoral research fellow in the School of Engineering and Mathematics, Edith Cowan University, Australia. He was a lecturer in the School of Computer Science and Engineering, International Islamic University Chittagong, Bangladesh. Dr. Ahmad is an IEEE member and his current research interest includes communication networks, computational intelligence and machine learning.
Joarder Kamruzzaman is currently a faculty member in the Faculty of Information Technology, Monash University, Australia. Before joining Monash University, Dr. Kamruzzaman worked with James Cook University, Australia and BUET, Bangladesh. He is an IEEE member and his research interest includes computer networks, bioinformatics and computational intelligence.
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