Adaptive admission control algorithm in a QoS-aware Web system

Adaptive admission control algorithm in a QoS-aware Web system

We present an adaptive service (or call) admission control algorithm (ASACA) for integrated services packet networks. The proposed algorithm does not require a priori knowledge of the source characteristics, and it consists of two steps. The first step adapts the service rate of the system based on the number of frames lost during the observation period, and the second step estimates the expected delay associated with each class of service upon admitting or rejecting a service in the system. For this purpose, we use an M/M/1 queueing model such that the current statistical delay is added to the error estimates of the delay scaled by its gradient. While admitting a service, we ensure that the delay commitments towards the requirement profile of ongoing services are preserved. Further the sum of the statistical arrival rates for each class of service and the peak rate of the new call is not allowed to exceed the current system service rate. Simulation experiments demonstrate that our adaptive service control algorithm, ASACA, performs twice as well as a non-adaptive algorithm using an M/M/1 model, in meeting the service requirements but at the cost of a reduced number of calls admitted into the system, the number of which depends on the system load.

The purpose of call admission control in Integrated Services Networks is to offer a guarantee that Quality of Service (QoS) bounds are not violated due to the admission of new calls into the network. This is typically accomplished using the declared worst-case traffic descriptors for incoming calls, a solution which results in poor bandwidth utilization. A measurement-based admission scheme is an appealing alternative: not only does it offer adaptivity to changing traffic conditions, it also allows statistical multiplexing gains to be exploited. In this paper, we examine the problem of determining which traffic characterization a measurement-based admission control algorithm should require of sources requesting access. Building on the work of Jamin et al. (1995), we also propose an adaptive measurement-based admission control algorithm that simplifies the estimation process, and show that it can achieve a high level of utilization without violating its delay-based QoS guarantees.

The paper proposes an adaptive admission control (AC) policy, that reduces call dropping probability, resorting to rate adaptation in accordance with the instantaneous connection configurations. We describe the philosophy of the proposed adaptive scheme and analyze the effective call dropping probability when considering a single cell UMTS scenario with an additive white Gaussian noise (AWGN) channel. Our simulations demonstrate that, by employing the proposed scheme, the network performance can be extremely increased while the quality of service (QoS) requested is respected.

This paper reports the outcome of an exhaustive analytical and numerical investigation of stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances. The class of adaptive algorithms considered are those commonly referred to as model-reference adaptive control algorithms, self-tuning controllers, and dead-beat adaptive controllers; they have been developed for both continuous-time systems and discrete-time systems. The existing adaptive control algorithms have been proven to be globally assymptotically stable under certain assumptions, the key ones being (a) that the number of poles and zeroes of the unknown plant are known, and (b) that the primary performance criterion is related to good command following. These theoretical assumptions are too restrictive from an engineering point of view. Real plants always contain unmodeled high-frequency dynamics and small delays, and hence no upper bound on the number of the plant poles and zeroes exists. Also real plants are always subject to unmeasurable output additive disturbances, although these may be quite small. Hence, it is important to critically examine the stability robustness properties of the existing adaptive algorithms when some of the theoretical assumptions are removed; in particular, their stability and performance properties in the presence of unmodeled dynamics and output disturbances. A unified analytical approach has been developed and documented in the recently completed Ph.D. thesis by Rohrs [15] that can be used to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite-gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. The practical engineering consequences of the existence of the infinite-gain operator are disastrous. Analytical and simulation results demonstrate that sinusoidal reference inputs at specific frequencies and/or sinusoidal output disturbances at any frequency (including d.c.) cause the loop gain of the adaptive control system to increase without bound, thereby exciting the (unmodeled) plant dynamics, and yielding an unstable control system. Hence, it is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

Developing closed-loop brain stimulation systems can benefit the treatment of neurological and neuropsychiatric disorders and facilitate brain functions. Current designs of closed-loop controllers have used time-invariant linear models of brain activity to devise non-adaptive controllers. However, unmodeled nonlinear dynamics can happen during real-time closed-loop control, leading to nonlinear uncertainty in the brain activity model. Current non-adaptive controllers cannot track the nonlinear model uncertainty and are not robust to noise, both of which can compromise their control performance. Here, within an ℒ1 adaptive control framework, we develop a new discrete-time robust and adaptive closed-loop control algorithm that addresses a general form of nonlinear model uncertainty. We conduct Monte Carlo simulations to validate the robust and adaptive control algorithm and show that it significantly outperforms existing closed-loop control algorithms. Our results can facilitate future designs of precise and safe closed-loop brain stimulation systems to treat neurological and neuropsychiatric disorders and modulate brain functions.

In the future, communications networks are expected to become less an end itself than a means to an end by exploiting them not only for telecommunications per se but also across other relevant economic sectors in order to reap larger benefits from interdisciplinary research across traditional borders, e.g., an increased overall reduction of greenhouse gas emissions across multiple sectors such as energy and transportation, as envisioned by the future Smart Grid. The two main quality attributes of a Smart Grid communications infrastructure are reliability and latency, as defined in IEEE P2030. This paper proposes to aggregate triple-play and Smart Grid services into a converged fiber-wireless (FiWi) broadband access network based on low cost Ethernet passive optical network (EPON) and wireless mesh networks. We first show that, as the load of the FiWi network increases, performance degradation in terms of packet drop and latency of Smart Grid applications occurs due to the lack of quality-of-service (QoS) protection. To mitigate this problem, we propose an adaptive admission control algorithm to provide QoS support for FiWi Smart Grid communications networks. Simulation results show that the proposed admission control enables QoS guarantees for triple-play applications as well as future Smart Grid applications over the same FiWi infrastructure.

In this paper a new distributed adaptive admission control algorithm for wireless cellular networks with heterogeneous traffic is proposed. The resources needed for handoff calls are estimated by calculating the probability of visiting particular cells during the call lifetime based on the call duration and residence time statistics. This information is then communicated to the base stations in the S surrounding rings. The amount of reserved resources is updated at handoffs and call termination which involves simple arithmetic operations only. The admission algorithm also maintains the tuning parameters by which hard constraints on handoff dropping probabilities can be met. We have also derived the QoS bounds for homogeneous traffic.

In this article we examine the architecture of an entity used for automatic management and provisioning of resources for DiffServ networks. We examine the existing literature and implementations in this area, focusing on the design choices made, and we propose an architecture for the design of Bandwidth Brokers that combines an adaptive admission control algorithm for increased utilization of network resources and a mechanism for reducing the complexity overhead that intends to be both simple and effective. Specifically, we present a novel architecture for the admission control module that aims at achieving a satisfactory balance between maximizing the resource utilization for the network provider and minimizing the overhead of the module. We complement our theoretical discussion with extensive experimental simulations for the proposed Bandwidth Broker components and analysis of the results. The simulations study the possible configurations of the proposed algorithm and also compare it with alternative admission control policies. Copyright © 2007 John Wiley & Sons, Ltd.

The model-free adaptive control (MFAC) which is based on compact from format dynamic linearization (CFDL) is applied in the speed control system of automatic door. The design of the adaptive controller is model free. It is directly based on the estimation and prediction of pseudo partial derivative (PPD). It analyzed and compared the effects of speed control which displayed by model free adaptive control and PID algorithm. And it also adaptive analyzed adaptive the effects when changing the parameters adaptive of pseudo partial derivative. The results of the adaptive experiments showed that the model-free adaptive control algorithm in the control of Auto-Door servo system is better than that of PID control. And the results also highlight the model free adaptive control algorithm for fast motion system stability and robustness.

This paper describes an adaptive position control algorithm of a DC motor based on the first order approximation using recursive least squares with forgetting. The main concept of the proposed control algorithm is that the relationship between single-input and single-output of the system can be approximated as a first order system in a particular time region. The approximated first order system can be used for optimal control instead of actual complicated DC motor dynamics. In order to approximate the input and output relationship of the system, the method of recursive least squares with forgetting has been used. The disturbance observer has been employed to estimate the error between actual DC motor system and approximated first order system. Based on the approximated first order system that represents the DC motor dynamics, the optimal voltage input has been computed using the Lyapunov direct method. The actual DC motor system equipped with encoder has been used for performance evaluation of the proposed control algorithm. The results show good control performance in terms of error convergence and adaption ability.

In this work12 we suggest real-time cost and performance metrics that are useful for survivability analysis at the radio layer of mobile networks. We demonstrate implementation of two of these metrics, to monitor blocking rates for adaptive admission control (AAC), using our simulation framework, to which we have added a data visualization (DV) layer. The DV layer uses 3-D animation to illustrate the effects of parameter selection on AAC performance, where the parameters of interest are those which control measurement and use of real-time metrics. Examples highlight the usefulness of the metrics to manage the tradeoff between new-call and handover channel requests and the capability of the DV layer to represent the spatial and temporal behavior of the AAC algorithm in a comprehensible form.

We present an adaptive control methodology that requires no plant modeling information. The method is based on a cumulative retrospective cost adaptive control algorithm, which is a direct adaptive control algorithm for stabilization, disturbance rejection, and command following when partial plant modeling information is available, specifically, the first nonzero Markov parameter, the relative degree, and estimates of nonminimum-phase zeros. The same adaptive algorithm is used online to estimate the required modeling information. By merging these processes into a single architecture, the resulting hybrid adaptive control algorithm requires no prior modeling information. The method is demonstrated on several illustrative disturbance rejection and command following problems, where the plant can be either minimum or nonminimum phase, and stable or unstable.

Wireless multimedia services have to rely on strict quality of service (QoS) requirements to reach the feasibility, including guaranteed available bandwidth, tight delay constraint, and hard jitter tolerance. WiMAX has emerged as one of the most promising broadband wireless access technologies to support these types of services. To guarantee the QoS of connections, CAC (Call Admission Control) can grant a new call only while the required bandwidth is available. With concern about connection capacity, the CAC seems rigid and turns into an obstacle instead while the bandwidth of some connections could be stolen temporarily. Thus, adaptive admission control, differing from the conventional CAC, can flexibly increase connection capacity by stealing bandwidth based on user streaming buffer (USB). A connection in sufficient USB state implies that the undergoing streaming application can perform smoothly even if its bandwidth is stolen temporarily. Therefore, how to determine the USB state of each connection is the most important topic in this study. In order to effectively decrease the risk of system overuse arising from stealing bandwidth, the adaptive admission control is extended by means of an intelligent mechanism to decide the USB states. The simulation results show the proposed intelligent mechanism utilizing fuzzy neural network (FNN) really outperforms than the adaptive admission control in terms of controlling the probability of poor application performance. With approximate connection capacity (or call blocking probability), the intelligent resource management indeed decreases the risk of system overuse.