Admission Control Policy Research Articles

The Throughput-Outage Tradeoff of Wireless One-Hop Caching Networks

We consider a wireless device-to-device (D2D) network where the nodes have pre-cached information from a library of available files. Nodes request files at random. If the requested file is not in the on-board cache, then it is downloaded from some neighboring node via one-hop "local" communication. An outage event occurs when a requested file is not found in the neighborhood of the requesting node, or if the network admission control policy decides not to serve the request. We characterize the optimal throughput-outage tradeoff in terms of tight scaling laws for various regimes of the system parameters, when both the number of nodes and the number of files in the library grow to infinity. Our analysis is based on Gupta and Kumar {\em protocol model} for the underlying D2D wireless network, widely used in the literature on capacity scaling laws of wireless networks without caching. Our results show that the combination of D2D spectrum reuse and caching at the user nodes yields a per-user throughput independent of the number of users, for any fixed outage probability in $(0,1)$. This implies that the D2D caching network is "scalable": even though the number of users increases, each user achieves constant throughput. This behavior is very different from the classical Gupta and Kumar result on ad-hoc wireless networks, for which the per-user throughput vanishes as the number of users increases. Furthermore, we show that the user throughput is directly proportional to the fraction of cached information over the whole file library size. Therefore, we can conclude that D2D caching networks can turn "memory" into "bandwidth" (i.e., doubling the on-board cache memory on the user devices yields a 100\% increase of the user throughout).

Preventing Fast Wear-out of Flash Cache with An Admission Control Policy

Recently, flash cache is widely adopted as the performance accelerator of legacy storage systems. Unlike other cache media, flash cache should be carefully managed as it has peculiar characteristics such as long write latency and limited P/E cycles. In particular, we make two prominent observations that can be utilized in managing flash cache. First, a serious worn-out problem happens when the working-set of a system is beyond the capacity of flash cache due to excessively frequent cache replacement. Second, more than 50% of data has no hit in flash cache as it is a second level cache. Based on these observations, we propose a cache admission control policy that does not cache data when it is first accessed, and inserts it into the cache only after its second access occurs within a certain time window. This allows the filtering of data disruptive to flash cache in terms of endurance and performance. With this policy, we prolong the lifetime of flash cache 2.3 times without any performance degradations.

Event-based optimization for admission control in distributed service system

In this paper, we consider the problem of admission control in a distributed service system. For the distributed structure of the system, service requests are assigned for service according to the node selection strategy. However, the admitted requests may be dropped since the service capacity of each service node in the system is limited. The desired admission control (AC) policy is to maximize the system performance while keep the dropping probability of the admitted requests below a certain threshold. The event-based optimization framework is applied to formulate the AC process. With the event-based policy, controllers at each service node make decisions only at the epoch when an event of request arrival occurs. The optimization of AC is thus modeled as a probabilistic constrained problem. We present the concept of risk-event to describe the event which happens may lead to dropping of the admitted request, and define the risk of system to denote the probability of a request been dropped. Using the Lagrangian approach, the original problem is converted to an unconstrained problem. Then, by applying the sensitivity analysis method, we derive the performance gradient formula with respect to the policy parameter based on event. Finally, numerical experiments are designed to verify the efficiency of the proposed method.

New Admission Control Policy for a Flexible VoLTE Service

LTE is the most recent mobile technology deployed everywhere in the whole wide world. It provides high Downlink and Uplink throughput for all service classes especially for voice class. All the services offered by LTE are based on IMS architecture. The goal of this paper is to present the developed admission control algorithm for VoLTE based on the Load control information and the UE capability in term of the supported voice codec. This developed algorithm has high performance results compared to the existing one that uses only G.711.

Who's Next? Scheduling Personalization Services with Variable Service Times

Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request is provided service before other waiting requests. The batching approach leads to sunk costs incurred when visitors wait for the next batch to begin. An optimal admission control policy is developed to prescreen new request arrivals. We show how the policy can be implemented efficiently when the revenue function is complex and there are a large number of requests that can be served in a batch. Numerical experiments show that the proposed approach leads to substantial improvements over a linear approximation of the concave revenue function. Interestingly, we find that the improvements in firm profits are not only (or primarily) due to the different service times that are obtained when using the nonlinear personalization function—there is a ripple effect on the admission control policy that incorporates these optimized service times, which contributes even more to the additional profits than the service time optimization by itself.

Dynamic admission control for two customer classes with stochastic demands and strict due dates

We study a dynamic capacity allocation problem with admission control decisions of a company that caters for two demand classes with random arrivals, capacity requirements and strict due dates. We formulate the problem as a Markov decision process (MDP) in order to find the optimal admission control policy that maximises the expected profit of the company. Such a formulation suffers a state-space explosion. Moreover, it involves an additional dimension arising from the multiple possible order sizes that customers can request which further increases the complexity of the problem. To reduce the cardinality of possible policies, and, thus, the computational requirements, we propose a threshold-based policy. We formulate an MDP to generate such a policy. To deal with the curse of dimensionality, we develop threshold-based approximate algorithms based on the state-reduction heuristics with aggregation proposed previously. Our results reveal that for the majority of instances considered the optimal policy has a threshold structure. We then demonstrate the superiority of the proposed threshold-based approximate algorithms over two benchmark policies in terms of the generated profits and the robustness of the solutions to changes in operational conditions. Finally, we show that our proposed policies are also robust to changes in actual demand from its estimation.

Admission Control for Multidimensional Workload input with Heavy Tails and Fractional Ornstein-Uhlenbeck Process

The infinite source Poisson arrival model with heavy-tailed workload distributions has attracted much attention, especially in the modeling of data packet traffic in communication networks. In particular, it is well known that under suitable assumptions on the source arrival rate, the centered and scaled cumulative workload input process for the underlying processing system can be approximated by fractional Brownian motion. In many applications one is interested in the stabilization of the work inflow to the system by modifying the net input rate, using an appropriate admission control policy. In this paper we study a natural family of admission control policies which keep the associated scaled cumulative workload input asymptotically close to a prespecified linear trajectory, uniformly over time. Under such admission control policies and with natural assumptions on arrival distributions, suitably scaled and centered cumulative workload input processes are shown to converge weakly in the path space to the solution of a d-dimensional stochastic differential equation driven by a Gaussian process. It is shown that the admission control policy achieves moment stabilization in that the second moment of the solution to the stochastic differential equation (averaged over the d-stations) is bounded uniformly for all times. In one special case of control policies, as time approaches ∞, we obtain a fractional version of a stationary Ornstein-Uhlenbeck process that is driven by fractional Brownian motion with Hurst parameter H > ½.

Admission Control for Multidimensional Workload input with Heavy Tails and Fractional Ornstein-Uhlenbeck Process

The infinite source Poisson arrival model with heavy-tailed workload distributions has attracted much attention, especially in the modeling of data packet traffic in communication networks. In particular, it is well known that under suitable assumptions on the source arrival rate, the centered and scaled cumulative workload input process for the underlying processing system can be approximated by fractional Brownian motion. In many applications one is interested in the stabilization of the work inflow to the system by modifying the net input rate, using an appropriate admission control policy. In this paper we study a natural family of admission control policies which keep the associated scaled cumulative workload input asymptotically close to a prespecified linear trajectory, uniformly over time. Under such admission control policies and with natural assumptions on arrival distributions, suitably scaled and centered cumulative workload input processes are shown to converge weakly in the path space to the solution of a d-dimensional stochastic differential equation driven by a Gaussian process. It is shown that the admission control policy achieves moment stabilization in that the second moment of the solution to the stochastic differential equation (averaged over the d-stations) is bounded uniformly for all times. In one special case of control policies, as time approaches ∞, we obtain a fractional version of a stationary Ornstein-Uhlenbeck process that is driven by fractional Brownian motion with Hurst parameter H > ½.

An Optimal Admission Control Protocol for Heterogeneous Multicast Streaming Services

We investigate optimal call admission control (CAC) policy for multicast streaming services (MSS) in 3rd generation (3G) and beyond wireless mobile networks. Several MSS sessions are supported simultaneously in a bandwidth-limited network. Active sessions are those that are currently serving some users, and inactive sessions are those that are currently not serving any users. An admission decision in MSS is required only when an inactive session is requested, unlike in unicasting. For this reason, if a user request for an inactive MSS session arrives, we should make an admission decision in anticipation of ( i ) the possible reward earned based on users served during a session active time generated by accepting it, and ( ii ) the influence of the session active time upon the future status of network bandwidth and admission decisions. Our objective is to determine when to admit or block a user asking an inactive MSS session to achieve the optimality in rewards. We formulate this problem as a semi-Markov decision process (SMDP), and a value iteration algorithm is used to obtain an optimal stationary deterministic policy. We also derive the user blocking probability of the optimal policy by analyzing an embedded Markov chain induced by it.

Measuring QoS Metrics in Femto/Macro Cellular Networks with CAC Policy

Due to the growing importance of deploying femtocell technology in the current mobile network operators, data traffic processing and handover (HO) management in such networks are challenging issues. Consequently, due to the mobility of femto users between indoor and outdoor environments, the HO traffic interplay between macrocell and femtocells should be addressed, by determining accurate HO rate and actual channel holding time. Classical methods for data traffic process and HO management cannot directly apply in such network. In this paper, we propose an adaptive call admission control (CAC) policy based on a pre-reservation (PR) area as well as we employ the concept of adaptive reserved channel to accomplish quality of service (QoS) requirements for handover traffic requests. A teletraffic model based on queuing theory and Markov chains will allow us to evaluate and quantify QoS metrics performance of the proposed scheme. The numerical results reveal that the benefits of femtocell technology integration with the current macrocellular mobile networks, especially in reducing the traffic loads of macrocell in terms of minimizing blocking probability.

Embedded Markov chain approach to retrial queue with vacation, phase repair and multioptional services

The investigation deals with the steady-state behavior of a batch arrival retrial queue with multi-optional services and phase repair under Bernoulli vacation schedule. The customers enter the system in batches and are admitted following Bernoulli admission control policy. The incoming customers are forced to join the retrial group if they find the server unavailable. The customers are served in two phases viz. first essential service (FES) followed by second optional services (SOS). The server is unreliable and if fails, it is repaired in d-compulsory phases so as to become as good as before failure. The server may go for a vacation after each service completion following Bernoulli vacation schedule or it may continue serving the next customer. By applying the embedded Markov chain method, we establish the ergodicity condition for the system. The steady-state formulae for some queueing measures are established by evaluating the generating functions of queue length distribution. The cost function of the system has also been formulated. Finally, the effects of various parameters on the performance of the system have been examined numerically.

Service Systems with Slowdowns: Potential Failures and Proposed Solutions

Many service systems exhibit service slowdowns when the system is congested. Our goal in this paper is to investigate this phenomenon and its effect on system performance. We modify the Erlang-A model to account for service slowdowns and carry out the performance analysis in the quality- and efficiencydriven (QED) regime. We find that when the load sensitivity is low, the system can achieve QED performance, but the square-root staffing parameter requires an adjustment to achieve the same performance as an ordinary Erlang-A queue. When the load sensitivity is high, the system alternates randomly between a QED and an efficiency-driven (ED) regime performance levels, a phenomenon that we refer to as bistability. We analyze how the system scale and model parameters affect the bistability phenomenon and propose an admission control policy to avoid ED performance.

Rate Adaptation and Admission Control for Video Transmission With Subjective Quality Constraints

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by 40% over conventional average-quality maximized rate-adaptation algorithms.

Call Admission Control Scheme and Handover Management in LTE Femtocell-Macrocell Integrated Networks

The femtocell technology is a quick and intelligent solution that improving system coverage and capacity to meet the great demand of services on broadband wireless access, the deployment of access point femtocells will unload a great quantity of traffic from the LTE macrocellular to be managed by the femtocellular network. Switching seamlessly between macrocell and femtocell base stations is a major challenge of LTE femtocell-macrocell integrated system; which is performed by the handover procedure that can guarantee an efficient transfer of UE from/to or between femtocells, and also offer an effective management of handover calls in the system. The intelligent LTE integrated system architecture, a generation and the optimization of neighboring list that contain the best cells for handover, efficient algorithm for call admission control, and also an optimized handover procedure are the most important topics for research. The intelligent LTE integrated system architecture is necessary to satisfy different criteria, as minimizing handover interruption, guarantee and assure minimum signaling overhead due to frequent and unnecessary handovers…so to determine a single handover decision making policy is not sufficient to develop the performance, thus in this paper we propose a call admission control policy to reduce the redundant handovers in the system and an improved process to create a neighboring list with best cells for handover and an appropriate ones for successful and efficient handover. Firstly, we present the LTE femtocell-macrocell integrated network architecture with a large deployment of femtocells. We propose the details signaling flux for handover procedure for different scenarios and the proposed CAC scheme to minimize the unnecessary handovers, based on an optimized neighboring list.

The ICU will see you now: efficient–equitable admission control policies for a surgical ICU with batch arrivals

Intensive Care Units (ICUs) are frequently the bottleneck in a hospital system, limiting patient flow and negatively impacting profits. This article examines admission control policies for a surgical ICU where patients arrive in batches. This problem is formulated as a Markov Decision Process (MDP) with an objective function that allows for varying degrees of emphasis on efficiency versus equity. Equity concerns are driven by a combination of surgery type and operating surgeon and are captured in a robust manner in the proposed models. A simple and efficient heuristic solution method related to our MDP formulation is proposed that provides a performance guarantee. The proposed admissions policy is applied to a real setting motivated by the cardiothoracic surgical ICU at Mount Sinai Medical Center in New York; the results demonstrate that the ICU can achieve large equity gains with no efficiency losses.

Optimal Switching Strategy between Admission Control and Pricing Control Policies with Two Types of Customers and Search Costs

This paper presents a switching strategy between the admission control and the pricing control policies in a queueing system with two types of customers. For an arriving first-type customer, the decision maker has an option on which policy to choose between the two control policies; that is, one determines whether or not to admit the customer’s request for the service (admission control) or decides a price of the customer’s request and offers it to the customer (pricing control). The second-type customers are only served when no first-type customers are present in the system in order to prevent the system from being idle. This would yield an extra income, which we refer to as the sideline profit. The so-called search cost, which is a cost paid to search for customers, creates the search option on whether to continue the search or not. We clarify the properties of the optimal switching strategy as well as the optimal search policy in relation to the sideline profit in order to maximize the total expected net profit. In particular, we show that when the sideline profit is sufficiently large, the two optimal switching thresholds exist with respect to the number of first-type customers in the system.

Utilization-based admission control for aperiodic tasks under EDF scheduling

Utilization-based admission control policies for processors executing aperiodic tasks are often favored due to their low overhead. This paper investigates utilization-based admission control for uniprocessor systems executing aperiodic tasks following the earliest deadline first priority assignment. We first propose a new constant time utilization-based admission control policy, CTAC. We prove that CTAC is safe and will admit any task instance that can be admitted by the best existing utilization-based admission control policy, ASL, given the same processor state. We also introduce an optimal utilization-based admission control policy, OPAC. It is proved that given the same processor state, a newly arriving task instance that can be admitted by any other safe utilization-based admission control policy can also be admitted by OPAC. We also show that OPAC cannot be implemented in constant time. Simulation results show that CTAC indeed outperforms ASL with constant time complexity and achieves performance close to OPAC in terms of various metrics.

Admission control of bulk retrial feedback queue with K-optional vacations

In this study, we have made an attempt to provide a ready reckoner for solving many congestion problems of day–to–day life under techno–economic constraints which can be seen in many industrial organisations and computer and communication systems. This can be made possible by designing a control policy called Bernoulli Admission Control Policy (BACP) wherein one can control the arrival rate of the customers by allowing only some of them to enter the system. We have applied this policy to a bulk retrial queueing system with Bernoulli feedback under Bernoulli vacation schedule. For the proper utilisation of the server's resources, there is a provision for the server to go for a fixed number of optional vacations (say K) depending upon its choice. We have provided a numerical simulation for solving the problem dealing with local area networks (LAN) especially relating to mobile IP. The service time, repair time, vacation time and setup time are assumed to be generally distributed. We have used supplementary variable technique (SVT) and probability generating function (PGF) method to compute various queueing and reliability measures of interest.

A reinforcement-learning approach for admission control in distributed network service systems

In the distributed network service systems such as streaming-media systems and resource-sharing systems with multiple service nodes, admission control (AC) technology is an essential way to enhance performance. Model-based optimization approaches are good ways to be applied to analyze and solve the optimal AC policy. However, due to "the curse of dimensionality", computing such policy for practical systems is a rather difficult task. In this paper, we consider a general model of the distributed network service systems, and address the problem of designing an optimal AC policy. An analytical model is presented for the system with fixed parameters based on semi-Markov decision process (SMDP). We design an event-driven AC policy, and the stationary randomized policy is taken as the policy structure. To solve the SMDP, both the state aggregation approach and the reinforcement-learning (RL) method with online policy optimization algorithm are applied. Then, we extend the problem by considering the system with time-varying parameters, where the arrival rates of requests at each service node may change over time. In view of this situation, an AC policy switching mechanism is presented. This mechanism allows the system to decide whether to adjust its AC policy according to the policy switching rule. And in order to maximize the gain of system, that is, to obtain the optimal AC policy switching rule, another RL-based algorithm is applied. To assess the effectiveness of SMDP-based AC policy and policy switching mechanism for the system, numerical experiments are presented. We compare the performance of optimal policies obtained by the solutions of proposed methods with other classical AC policies. The simulation results illustrate that higher performance and computational efficiency could be achieved by using the SMDP model and RL-based algorithms proposed in this paper.

Call Admission Control for Soft Handoff Coverage in CDMA Cellular System

In code division multiple access mobile cellular networks, the transmission quality can be improved by using the soft handoff techniques. The present investigation is concerned with the admission control policies by exploiting the soft handoff coverage area of the cellular radio network. We suggest finite Markov models for the admission control based on reserve channel and sub-rating policies. In reserve channel policy, a fixed number of channels are reserved exclusively to serve the handoff calls whereas according to sub-rating policy, the occupied channels can be split into two half rate channels to accommodate more handoff voice calls. It is also considered that the new call generation rate per unit area is uniformly distributed over the service area. Various performance indices are derived in terms of steady state probabilities. For some special situations, we validate and compare the models developed with previous existing models by setting the system descriptors. To examine the effect of various parameters on the performance measures, the sensitivity analysis is carried out by taking illustrations.