 Research Article
 Open Access
An Efficient Method for Proportional Differentiated Admission Control Implementation
 Vladimir Shakhov^{1} and
 Hyunseung Choo^{2}Email author
https://doi.org/10.1155/2011/738386
© Vladimir Shakhov and Hyunseung Choo. 2011
 Received: 14 November 2010
 Accepted: 11 February 2011
 Published: 8 March 2011
Abstract
The admission control mechanism inspired in the framework of proportional differentiated services has been investigated. The mechanism provides a predictable and controllable network service for realtime traffic in terms of blocking probability. Implementation of proportional differentiated admission control is a complicated computational problem. Previously, asymptotic assumptions have been used to simplify the problem, but it is unpractical for realworld applications. We improve previous solutions of the problem and offer an efficient nonasymptotic method for implementation of proportional differentiated admission control.
Keywords
 Admission Control
 Asymptotic Approximation
 Blocking Probability
 Call Admission Control
 Limited Regime
1. Introduction
Efficient implementation of admission control mechanisms is a key point for nextgeneration wireless network development. Actually, over the last few years an interrelation between pricing and admission control in QoSenabled networks has been intensively investigated. Call admission control can be utilized to derive optimal pricing for multiple service classes in wireless cellular networks [1]. Admission control policy inspired in the framework of proportional differentiated services [2] has been investigated in [3]. The proportional differentiated admission control (PDAC) provides a predictable and controllable network service for realtime traffic in terms of blocking probability. To define the mentioned service, proportional differentiated service equality has been considered and the PDAC problem has been formulated. The PDAC solution is defined by the inverse Erlang loss function. It requires complicated calculations. To reduce the complexity of the problem, an asymptotic approximation of the Erlang B formula [4] has been applied. However, even in this case, the simplified PDAC problem remains unsolved.
In this paper, we improve the previous results in [3] and withdraw the asymptotic assumptions of the used approximation. It means that for the desired accuracy of the approximate formula an offered load has to exceed a certain threshold. The concrete value of the threshold has been derived. Moreover, an explicit solution for the considered problem has been provided. Thus, we propose a method for practical implementation of the PDAC mechanism.
The rest of the paper is organized as follows. In the next section, we give the problem statement. In Section 3, we first present a nonasymptotic approximation of the Erlang B formula. We then use it for a proportional differentiated admission control implementation and consider some alternative problem statements for an admission control policy. In Section 4, we present the results of numerous experiments with the proposed method. Section 5 is a brief conclusion.
2. Problem Statement
 (i)
: is a number of traffic classes. ;
 (ii)
: is the weight of class . This parameter reflects the traffic priority. By increasing the weight, we also increase the admittance priority of corresponding traffic class;
 (iii)
: is the offered load of class traffic;
 (iv)
, is an allotted partition of the link capacity, is a bandwidth requirement of class connections, and is the largest integer not greater than ;
 (v)
: is the Erlang loss function, that is, under the assumptions of exponential arrivals and general session holding times [5], it is the blocking probability for traffic of class .
In practice, the limited regime (7) is not appropriate. But the simplification (8) can be used without the conditions (7). Actually, the approximation (6) can be applied without the condition (4). We prove it below.
3. Offered Technique
3.1. Approximate Erlang B Formula
We assert that for the desired accuracy of the approximation (6) an offered load has to exceed a certain threshold. The concrete value of the threshold is given by the following theorem.
Theorem 1.
Proof.
From the inequality (20), we obtainthe condition (9).
The proof is completed.
Note that the approximate formula (6) can provide the required accuracy in the case of . Actually, if , then the required accuracy is reached for . Thus, the condition (9) is sufficient but not necessary. It guarantees the desired accuracy of the approximation for any small and .
3.2. PDAC Solution
Thus, the formulas (23)–(25) provide the implementation of proportional differentiated admission control.
It is clear that for some values , we can obtain in (24) or in (23). Therefore, the problem is unsolvable and PDAC implementation is impossible for the given parameters.
It follows from the theorem that the approximation (6) is applicable even for and any small if . In spite of this fact, the solution above cannot be useful for small values of the ratio . In this case, the loss function is sensitive to fractional part dropping under calculation . For example, if kb/s, , and we obtain kb/s, then the approximate value of the blocking probability is about 0.004. But and . Thus, the offered approximate formula is useful if the ratio is relatively large.
3.3. Alternative Problem Statements
Let be the number of channel assigned for class traffic, . Each class is characterized by a worstcase loss guarantee [7, 8].
Thus, the optimization problem (35) is reduced to the problem (1).
4. Performance Evaluation
Table 1
Class  , kb/s 




1  130887  1022  0.0803  0.0803 
2  129786  1013  0.0877  0.079 
3  128409  1003  0.0961  0.0769 
4  126639  989  0.108  0.0756 
5  124279  970  0.1243  0.0746 
If an obtained accuracy is not enough, then the formulas (23)–(25) provide efficient first approximation for numerical methods.
5. Conclusion
In this paper, a simple nonasymptotic approximation for the Erlang B formula is considered. We find the sufficient condition when the approximation is relevant. The proposed result allows rejecting the previously used limited regime and considers the proportional differentiated admission control under finite network resources. Following this way, we get explicit formulas for PDAC problem. The proposed formulas deliver highperformance computing of network resources assignment under PDAC requirements. Thus, an efficient method for proportional differentiated admission control implementation has been provided.
Declarations
Acknowledgments
This research was supported in part by MKE and MEST, Korean government, under ITRC NIPA2010(C109010210008), FTDP(20100020727), and PRCP(20100020210) through NRF, respectively. A preliminary version of this paper was presented at MACOM 2010, Spain (Barcelona) [10]. The present version includes additional mathematical and numerical results.
Authors’ Affiliations
References
 Yilmaz O, Chen IR: Utilizing call admission control for pricing optimization of multiple service classes in wireless cellular networks. Computer Communications 2009, 32(2):317323. 10.1016/j.comcom.2008.11.001View ArticleGoogle Scholar
 Dovrolis C, Stiliadis D, Ramanathan P: Proportional differentiated services: delay differentiation and packet scheduling. IEEE/ACM Transactions on Networking 2002, 10(1):1226. 10.1109/90.986503View ArticleGoogle Scholar
 Salles RM, Barria JA: Proportional differentiated admission control. IEEE Communications Letters 2004, 8(5):320322. 10.1109/LCOMM.2004.827384View ArticleGoogle Scholar
 Jagerman DL: Some properties of the Erlang loss function. Bell System Technical Journal 1974, 53(3):525551.MathSciNetView ArticleMATHGoogle Scholar
 Bertsekas D, Gallager R: Data Networks. 2nd edition. Prentice Hall, Englewood Cliffs, NJ, USA; 1992.MATHGoogle Scholar
 Harrel A: Sharp bounds and simple approximations for the Erlang delay and loss formulas. Management Sciences 1988, 34(8):959972. 10.1287/mnsc.34.8.959View ArticleGoogle Scholar
 Christin N, Liebeherr J, Abdelzaher T: Enhancing classbased service architectures with adaptive rate allocation and dropping mechanisms. IEEE/ACM Transactions on Networking 2007, 15(3):669682.View ArticleGoogle Scholar
 Koo J, Shakhov VV, Choo H: An Enhanced REDBased Scheme for Differentiated Loss Guarantees, Lecture Notes in Computer Science. Volume 4238. Springer, New York, NY, USA; 2006.Google Scholar
 Haring G, Marie R, Puigjaner R, Trivedi K: Loss formulas and their application to optimization for cellular networks. IEEE Transactions on Vehicular Technology 2001, 50(3):664673. 10.1109/25.933303View ArticleGoogle Scholar
 Shakhov VV: An efficient method for proportional differentiated admission control implementation. Proceedings of the 3rd International Workshop on Multiple Access Communications (MACOM '10), September 2010, Barcelona, Spain 9197.View ArticleGoogle Scholar
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