Admission Control Policies in Loss Networks

For the design of call admission control (CAC) policy in wireless networks, how to decrease the average system cost is a key issue. In this paper, we study the optimal admission policy for minimizing the system cost. By modeling the admission control problem into a Markov decision process (MDP) and analyzing the corresponding value function, we obtain some monotonicity properties of the optimal policy. These properties suggest that the optimal admission control policy for the bandwidth asymmetry wireless networks should have a threshold structure and the threshold specified for a class of calls may change with the system state. Due to the prohibitively high complexity for computing the thresholds in a system with large state space, we propose a heuristic CAC policy called call-rate- based dynamic threshold (CRDT) policy to approximate the theoretical optimal policy based on the insights we obtain from the modeling and the analytical study on the properties of the optimal policy. The CRDT policy is efficient and can be easily implemented. The numerical results show that the performance of average system cost of the proposed CRDT policy is close to that of the optimal policy from the MDP model and is better than that of some known existing CAC schemes, including those performing well in bandwidth asymmetry wireless networks.

In this paper, we use the event-based optimization framework to study the admission control problem in an open Jackson network. The external arriving customers are controlled by an admission controller. The controller makes decision only at the epoch when an event of customer arrival happens. Every customer in the network obtains a fixed reward for each service completion and pays a cost with a fixed rate during its sojourn time (including waiting time and service time). The complete information of the system state is not available for the controller. The controller can observe only the number of total customers in the network. Based on the property of closed form solution of Jackson networks, we prove that the system performance is monotonic with respect to the admission probabilities and the optimal control policy has a threshold form. That is, when the number of total customers is smaller than a threshold, all of the arriving customers are admitted; otherwise, all are rejected. The sufficient condition and necessary condition of optimal policy are also derived. Furthermore, we develop an iterative algorithm to find the optimal policy. The algorithm can be executed based on a single sample path, which makes the algorithm online implementable. Simulation experiments are conducted to demonstrate the effectiveness of our approach.

Optimal admission control in two-class preemptive loss systems

In the near future, demand for heterogeneous wireless networking (HWN) is expected to increase. QoS provisioning in these networks is a challenging issue considering the diversity in wireless networking technologies and the existence of mobile users with different communication requirements. In HWNs with their increased complexity, “the curse of dimensionality” problem makes it impractical to directly apply the decision theoretic optimal control methods that are previously used in homogeneous wireless networks to achieve desired QoS levels. In this paper, optimal call admission control policies for HWNs are considered. A decision theoretic framework for the problem is derived by a dynamic programming formulation. We prove that for a two-tier wireless network architecture, the optimal policy has a two-dimensional threshold structure. Further, this structural result is used to design two computationally efficient algorithms, Structured Value Iteration and Structured Update Value Iteration. These algorithms can be used to determine the optimal policy in terms of thresholds. Although the first one is closer in its operation to the conventional Value Iteration algorithm, the second one has a significantly lower complexity. Extensive numerical observations suggest that, for all practical parameter sets, the algorithms always converge to the overall optimal policy. Further, the numerical results show that the proposed algorithms are efficient in terms of time-complexity and in achieving the optimal performance.

In CDMA systems, although the technique of successive interference cancellation (SIC) can help to reduce multiple access interference (MAI) and increase system capacity, radio resources are still scarce due to the rapid increment of mobile users and the high requirements of communication services. Therefore, the policy of admission control (AC) needs to be carefully studied in CDMA systems with SIC. Moreover, multiple factors such as the differentiated services provided for different classes of users, single user power constraint, total received power constraint at the base station (BS) and decoding order have heavy impact on the system performance, and thus should be considered during designing the AC policy. In this paper, we study the AC policy in CDMA systems with SIC and propose an AC algorithm based on grade of service (GoS) to set the admission threshold under both constraints of the single user's power and total received power at the BS. In addition, the decoding order adjustment with the power constraint is taken into account in the proposed algorithm. As a result, our proposed AC algorithm benefits to improve the overall system performance.

The design criterion of call admission control policy in heterogeneous wireless networks (HWN) is to make full use of the spectrum resources in different radio access systems, under the constraint of seamless QoS. In order to overcome the shortcoming of the traditional optimal guard channel policy (OGCP) in providing seamless QoS, a definition of balance channel is proposed firstly, and then a QoS fairness factor is defined to measure the difference in QoS level between different areas of the HWN. Accordingly, a seamless QoS-aware call admission control policy in HWN is proposed, which is called the balance and guard channel policy (BGCP). In BGCP, the admission control problem is formulated as an optimization problem under the constraint of QoS fairness factor, and the optimizing object is to minimize the grade of service (GoS) of the HWN. Simulation results show that, in comparison with OGCP, the proposed admission control policy successfully provides the seamless QoS guarantee for the multi-mode mobile terminals, at the cost of a very little decrease in resource utilization.

Admission control is a fundamental mechanism for the provisioning of quality of services in multiservice networks. The control of multi-scaling traffic demands specially designed mechanism given its highly bursty nature, especially at small time scales. In this paper, a greedy policy for the admission control of multi-scaling traffic is introduced. This policy accepts flows into the network according to a decreasing order of revenue. It is shown that results produced by this new policy are close to the one produced by an optimum policy.

Tangential hand velocity profiles of rapid human arm movements often appear as sequences of several bell-shaped acceleration-deceleration phases called submovements or movement units. This suggests how the nervous system might efficiently control a motor plant in the presence of noise and feedback delay. Another critical observation is that stochastic-ity in a motor control problem makes the optimal control policy essentially different from the optimal control policy for the deterministic case. We use a simplified dynamic model of an arm and address rapid aimed arm movements. We use reinforcement learning as a tool to approximate the optimal policy in the presence of noise and feedback delay. Using a simplified model we show that multiple submovements emerge as an optimal policy in the presence of noise and feedback delay. The optimal policy in this situation is to drive the arm's end point close to the target by one fast submovement and then apply a few slow submovementsto accurately drive the arm's end point into the target region. In our simulations, the controller sometimes generates corrective submovements before the initial fast submovement is completed, much like the predictive corrections observed in a number of psychophysical experiments.

Technologies that enable network slicing are expected to be a key component of next generation mobile networks. Their promise lies in enabling tenants (such as mobile operators and/or services) to reap the cost and performance benefits of sharing resources while retaining the ability to customize their own allocations. When employing dynamic sharing mechanisms, tenants may exhibit strategic behavior, optimizing their choices in response to those of other tenants. This paper analyzes dynamic sharing in network slicing when tenants support inelastic users with minimum rate requirements. We propose a NEtwork Slicing (NES) framework combining: 1) admission control; 2) resource allocation; and 3) user dropping. We model the network slicing system with admitted users as a NES game; this is a new class of game where the inelastic nature of the traffic may lead to dropping users whose requirements cannot be met. We show that, as long as admission control guarantees that slices can satisfy the rate requirements of all their users, this game possesses a Nash equilibrium. Admission control policies (a conservative and an aggressive one) are considered, along with a resource allocation scheme and a user dropping algorithm, geared at maintaining the system in Nash equilibria. We analyze our NES framework's performance in equilibrium, showing that it achieves the same or better utility than static resource partitioning, and bound the difference between NES and the socially optimal performance. Simulation results confirm the effectiveness of the proposed approach.

It is well known that highly volatile control laws, while theoretically optimal for certain systems, are undesirable from an engineering perspective, being generally deleterious to the controlled system. In this article we are concerned with the temporal volatility of the regulator's control process in discrete time Linear Quadratic Regulators (LQRs). Our investigation in this paper unearths a surprising connection between the cost functional that an LQR is tasked with minimizing and the temporal variations of its control laws. We first show that optimally controlling the system always implies high levels of control volatility, i.e., it is impossible to reduce volatility in the optimal control process without sacrificing cost. We also show that, akin to communication systems, every LQR has a Capacity Region associated with it, that dictates and quantifies how much cost is achievable at a given level of control volatility. This additionally establishes the fact that no admissible control policy can simultaneously achieve low volatility and low cost. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare. Our investigation in this paper unearths a surprising connection between the cost functional that an LQR is tasked with minimizing and the temporal variations of its control laws. We first show that optimally controlling the system always implies high levels of control volatility, i.e., it is impossible to reduce volatility in the optimal control process without sacrificing cost. We also show that, akin to communication systems, every LQR has a Capacity Region associated with it, that dictates and quantifies how much cost is achievable at a given level of control volatility. This additionally establishes the fact that no admissible control policy can simultaneously achieve low volatility and low cost. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare. We then employ this analysis to explain the phenomenon of extreme temporal price volatility, frequently observed in deregulated electricity markets. We model the suppliers and consumers in the market as strategic, price-anticipating participants who submit bids, in the form of price-quantity graphs, to the market operator. We then show that in any such market, at the Nash-Walras equilibrium, improving social welfare must necessarily be traded off with volatility in prices, i.e., it is impossible to reduce volatility in the price of electricity without sacrificing social welfare.

CAC-RD (call admission control based on reservation and diagnosis) [1] is call admission control (CAC) for UMTS (universal mobile terrestrial system) 3G networks. It is based on two schemes: channel reservation and network diagnosis. When compared to other CAC mechanisms, CAC-RD can guarantee network availability, reducing priority classes blocking and guarantying some network QoS requirements. Due to computational resource limits, simulations cannot answer related to admission control in big networks with thousands of users. This work presents a method to extrapolate scientific questions like CAC's behavior with thousands of users and many antennas. An artificial neural network (ANN) approach for CAC-RD in UMTS 3G networks is presented. Its main goal is ANN training with the CAC-RD behavior. It is expected that the ANN can answer questions related to real cellular 3G networks admission control.

In complex manufacturing environments, meeting the due dates of the jobs and minimizing in process inventories are important performance metrics. One of the common characteristics of complex production systems is production-assembly network of operations. This paper presents an auction-based algorithm for simultaneous scheduling of all manufactured and assembled jobs in a dynamic environment, where the objective function is to minimize both the due date penalties associated with the final products and the inventory cost of the work in process. An auction-based approach using a Mixed-Integer Linear Programming (MILP) model to construct and evaluate the bids so that the auction mechanism mimics a Lagrangian relaxation-based subgradient optimization to ensure global optimality is proposed. The inner structure of the problem enables very efficient calculation of bids for each job or assembly. Using a full factorial experimental design the properties of the proposed algorithm are analyzed. Results show that the proposed auction based algorithm performs better than the popular dispatching rules and is more scalable than the MILP model or direct implementations of the subgradient algorithm. Furthermore, the proposed algorithm is designed to work in a dynamic environment.

Uplink admission and power control in a CDMA wireless network are investigated. The subject is first reviewed and admission strategies classification is recalled. We propose a feasibility-based call admission control algorithm. This algorithm is compared to a received power-based admission control in a simple simulation. Performance is represented by the grade of annoyance, which is a combination of blocking and dropping probabilities. The best performance of each of the two studied algorithms is evaluated as a function of the offered traffic. The proposed feasibility-based admission control is found to have some performance improvements.

We study the stochastic user scheduling and power control problem for an uplink multi-user network over time-varying channels, where the users randomly harvest renewable energies from the environment. For each user, the renewable energies and arriving data packets with a constant rate are stored in energy (battery) and data buffers, respectively. Users have statistical packet delay constraints in terms of maximum acceptable delay-outage probabilities. We classify the users as prioritized and non-prioritized users. Our goal is to maximize the arrival rate of the non-prioritized user while supporting the minimum data rate requirements for the prioritized users. We reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis and study the optimal scheduling and power control policy. Since the optimal policy requires centralized processing with high computational complexity, we develop a reduced-complexity distributed algorithm, which can be implemented at each individual user. Online algorithm is devised, which does not require the statistical knowledge of the channel fading and energy harvesting (EH) processes. Numerical results demonstrate the effectiveness of the centralized and distributed schemes for different delay constraints and EH settings.

The complementary characteristics of wireless cellular networks and wireless local area networks (WLANs) make them suitable to jointly offer seamless wireless access services to mobile users. In an integrated cellular/WLAN system, the quality-of-service (QoS) requirements for different services (e.g., voice, real-time video) require admission control to limit the number of connections in each access network. In this paper, we first develop an analytical model to facilitate the evaluation of different admission control policies in a multiservice integrated cellular/WLAN system. We then formulate two different revenue-maximization problems. Each problem takes different QoS requirements into account. By solving the equivalent cost-minimization problems, we evaluate the system performance when different combinations of cutoff priority and fractional guard channel admission control policies are being used. Results show that using a cutoff priority policy in both wireless access networks can achieve the optimal solution for the two optimization problems under a wide range of network conditions.