- Research Article
- Open Access
An Efficient Method for Proportional Differentiated Admission Control Implementation
© Vladimir Shakhov and Hyunseung Choo. 2011
- Received: 14 November 2010
- Accepted: 11 February 2011
- Published: 8 March 2011
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 real-time 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 real-world applications. We improve previous solutions of the problem and offer an efficient nonasymptotic method for implementation of proportional differentiated admission control.
- Admission Control
- Asymptotic Approximation
- Blocking Probability
- Call Admission Control
- Limited Regime
Efficient implementation of admission control mechanisms is a key point for next-generation wireless network development. Actually, over the last few years an interrelation between pricing and admission control in QoS-enabled networks has been intensively investigated. Call admission control can be utilized to derive optimal pricing for multiple service classes in wireless cellular networks . Admission control policy inspired in the framework of proportional differentiated services  has been investigated in . The proportional differentiated admission control (PDAC) provides a predictable and controllable network service for real-time 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  has been applied. However, even in this case, the simplified PDAC problem remains unsolved.
In this paper, we improve the previous results in  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.
: is the Erlang loss function, that is, under the assumptions of exponential arrivals and general session holding times , 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.
3.1. 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.
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 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
Thus, the optimization problem (35) is reduced to the problem (1).
If an obtained accuracy is not enough, then the formulas (23)–(25) provide efficient first approximation for numerical methods.
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 high-performance computing of network resources assignment under PDAC requirements. Thus, an efficient method for proportional differentiated admission control implementation has been provided.
This research was supported in part by MKE and MEST, Korean government, under ITRC NIPA-2010-(C1090-1021-0008), FTDP(2010-0020727), and PRCP(2010-0020210) through NRF, respectively. A preliminary version of this paper was presented at MACOM 2010, Spain (Barcelona) . The present version includes additional mathematical and numerical results.
- Yilmaz O, Chen IR: Utilizing call admission control for pricing optimization of multiple service classes in wireless cellular networks. Computer Communications 2009, 32(2):317-323. 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):12-26. 10.1109/90.986503View ArticleGoogle Scholar
- Salles RM, Barria JA: Proportional differentiated admission control. IEEE Communications Letters 2004, 8(5):320-322. 10.1109/LCOMM.2004.827384View ArticleGoogle Scholar
- Jagerman DL: Some properties of the Erlang loss function. Bell System Technical Journal 1974, 53(3):525-551.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):959-972. 10.1287/mnsc.34.8.959View ArticleGoogle Scholar
- Christin N, Liebeherr J, Abdelzaher T: Enhancing class-based service architectures with adaptive rate allocation and dropping mechanisms. IEEE/ACM Transactions on Networking 2007, 15(3):669-682.View ArticleGoogle Scholar
- Koo J, Shakhov VV, Choo H: An Enhanced RED-Based 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):664-673. 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 91-97.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.