Queue Stability Research Articles

Dynamic Optimization of Active Queue Management Routers to Improve Queue Stability

This paper aims to introduce the numerical methods for solving the optimal control theory to model bufferbloat problem. Mathematical tools are useful to provide insight for system engineers and users to understand better about what we are facing right now while experiment in a large-scale testbed can encourage us to implement in realistic scenario. In this paper, we introduce a survey of the numerical methods for solving the optimal control problem. We propose the dynamic optimization sweeping algorithm for optimal control of the active queue management. Simulation results in network simulator ns2 demonstrate that our proposed algorithm can obtain the stability faster than the others while still maintain a short queue length (≈10 packets) and low delay experience for arriving packets (0.4 seconds).

Asymptotic Bounds on the Power-Delay Tradeoff for Fading Point-to-Point Links From Geometric Bounds on the Stationary Distribution of the Queue Length

The optimal power-delay tradeoff is studied for a time-slotted independently and identically distributed fading point-to-point link, with perfect channel state information at both transmitter and receiver, and with random packet arrivals to the transmitter queue. It is assumed that the transmitter can control the number of packets served by controlling the transmit power in the slot. The optimal tradeoff between average power and average delay is analyzed for stationary and monotone transmitter policies. For such policies, an asymptotic lower bound on the minimum average delay of the packets is obtained, when average transmitter power approaches the minimum average power required for transmitter queue stability. The asymptotic lower bound on the minimum average delay is obtained from geometric upper bounds on the stationary distribution of the queue length. This approach, which uses geometric upper bounds, also leads to an intuitive explanation of the asymptotic behavior of average delay. The asymptotic lower bounds, along with previously known asymptotic upper bounds, are used to identify three new cases where the order of the asymptotic behavior differs from that obtained from a previously considered approximate model, in which the transmit power is a strictly convex function of real valued service batch size for every fade state.

Measuring the impact of adversarial errors on packet scheduling strategies

In this paper, we explore the problem of achieving efficient packet transmission over unreliable links with worst-case occurrence of errors. In such a setup, even an omniscient offline scheduling strategy cannot achieve stability of the packet queue, nor is it able to use up all the available bandwidth. Hence, an important first step is to identify an appropriate metric to measure the efficiency of scheduling strategies in such a setting. To this end, we propose an asymptotic throughput metric which corresponds to the long-term competitive ratio of the algorithm with respect to the optimal. We then explore the impact of the error detection mechanism and feedback delay on our measure. We compare instantaneous with deferred error feedback, which requires a faulty packet to be fully received in order to detect the error. We propose algorithms for worst-case adversarial and stochastic packet arrival models, and formally analyze their performance. The asymptotic throughput achieved by these algorithms is shown to be close to optimal by deriving lower bounds on the metric and almost matching upper bounds for any algorithm in the considered settings. Our collection of results demonstrate the potential of using instantaneous feedback to improve the performance of communication systems in adverse environments.

Heavy-traffic asymptotics of a priority polling system with threshold service policy

In this paper, by the singular-perturbation technique, we investigate the heavy-traffic behavior of a priority polling system with three queues under threshold policy. It turns out that the scaled queue-length of the critically loaded queue is exponentially distributed, independent of that of the stable queues, which possess the same distributions as a two-class priority queue with N-policy vacation. Further, we provide an approximation of the tail queue-length distribution of the stable queues, which shows that it has the same prefactors and decay rates as the classical two-class preemptive priority queue. Stochastic simulations are taken to support the results.

Distributed Queue-Aware Relay Node Selection for Cooperative Wireless Networks via Vickrey Auction Game

Queue-aware relay selection for the uplink of a user cooperation multiple access wireless network is investigated. To achieve better overall quality-of-service (QoS), the queue states of the relay buffers are considered in the relay selection process. A distributed relay selection mechanism based on a Vickrey auction game is proposed. It is shown that with a strictly positive reserve price, the unique Nash equilibrium of the auction game achieved by a truthful bidding strategy is the best relay response under the queue length constraint. Numerical results show that a significant improvement in QoS provisioning is achieved using the proposed auction-based queue-aware relay selection, compared to fixed relaying and channel-aware relay selection, especially when the relays have high average packet arrival rates. Given a target system packet blocking rate, the proposed scheme helps to better maintain relay buffer queue stability.

Stochastic stable buffer control for quality-adaptive HEVC video transmission in enterprise WLAN architectures

Transmission of high efficiency video coding (HEVC) video streams over error-prone enterprise wireless local area networks (WLAN) architectures is challenging because of the difficulties in buffer overflow management in the switches within enterprise WLAN architectures. Thus, this paper proposes a new quality-aware video transmission method for company-wide enterprise WLAN architectures by combining video transmission technologies with a distributed stochastic buffering model that jointly controls power consumption and queue stabilization. After conducting extensive simulations with HEVC test sequences, significant video quality improvements are observed with the average Y-PSNR gain of 3.33 dB.

On the deterministic approach to active queue management

Virtually all known active queue management (AQM) algorithms, except for the two-category classifier (TCC), operate by calculating packet dropping probabilities. The probabilistic approach involves the necessity of using a type of random number generation upon every packet arrival at the router. Even if the generation of a single random value does not involve high computational complexity, the overhead becomes significant, considering the number of packets in a typical Internet environment. We propose a new AQM algorithm based on the deterministic approach. The algorithm offers a high throughput and a low loss ratio while maintaining a short and stable queue size. At the same time, the algorithm is of low computation complexity, which allows for energy-efficient implementations in routers. In addition to that, the proposed algorithm is universal--it provides high performance in a variety of distinct networking scenarios (diversified round-trip times, congestion levels, traffic types, etc.). Contrary to the TCC algorithm, the proposed algorithm does not make the decision whether to accept or drop the packet upon each packet arrival--the computation process is involved less frequently.

Proof of the conjecture on the stability of a multiserver retrial queue

In this paper we solve the conjecture made by Avram, Matei and Zhao (2014), on stability condition of an M/M/s retrial queue with Bernoulli acceptance, abandonment and feedback. The Markov process describing this queueing system is positive recurrent if ρ∞<1 and transient if ρ∞>1, where ρ∞ is the traffic load under the saturation condition of the orbit. We also investigate the critical case when ρ∞=1 to see if it can be either stable or unstable.

Waiting in Line: Norms, Markets, &amp; the Law

This Article examines the rules and practices of waiting in line as a system of informal order, showing that despite its reputation for drudgery, the queue offers rich insights about social norms and the psychology of cooperation. The Article begins by investigating the implicit customs of physical waiting line, uncovering the surprisingly complex unwritten rules (and exceptions) that give queues stability even in the absence of legal governance or state enforcement. Yet the prevailing norms literature typically explains informal order by reference to close-knit groups that can impose sanctions on violators of extralegal rules. This raises a puzzle: Why do queue norms repeatedly produce informal, yet reliable, order among total strangers unlikely to interact again? This Article answers this question by looking to social-psychological research showing that people tend to be strong reciprocators rather than selfish utility maximizers. This model makes sense of both our tendency to defer to line norms as well as the disproportionate sanctions with which defectors from these norms meet.

Stable Queue Management in communication networks

Since Active Queue Management (AQM) was recommended by the Internet Engineering Task Force (IETF) as an efficient way to overcome performance limitations of Transmission Control Protocol (TCP), several studies have proven control theory to be a promising field for the design and analysis of congestion control in homogenous communication networks. AQM is gaining increased importance due to reports of buffer-induced latencies throughout the Internet. The increasing volume and diversity of traffic types (i.e., data, voice, and video) suggests that traffic management mechanisms, in general, and AQM schemes, specifically, must not only focus on the critical issue of congestion control but must also consider the QoS demands of heterogeneous traffic. However, to combine quality-of-service provisioning with congestion control, AQM design needs to be reconsidered. In this paper, we propose a state feedback controller design scheme for heterogeneous networks preserving the closed-loop system stability. Delay dependant stability conditions of the closed loop system are derived based on the Lyapunov-Krasovskii method. The proposed approach offers flexible choice of control parameters allowing the network administrator to control fairness and response time for each individual source node in a network of multiple links with different delay properties. The performance and robustness of the proposed controller were illustrated and analyzed using event-based computer simulations.

Phase Plane Analysis of Quantized Congestion Notification for Data Center Ethernet

Currently, Ethernet is being enhanced to become the unified switch fabric in data centers. With the unified switch fabric, the cost on redundant devices is reduced, while the design and management of data center networks are simplified. Congestion management is one of the indispensable enhancements on Ethernet, and Quantized Congestion Notification (QCN) has just been ratified as the formal standard. Though QCN has been investigated for several years, there exist few in-depth theoretical analyses on QCN. The most possible reason is that QCN is heuristically designed and involves the property of variable structure. The classic linear analysis method is incapable of handling the segmented nonlinearity of the variable structure system. In this paper, we use the phase plane method, which is suitable for systems of segmented nonlinearity, to analyze the QCN system. The overall dynamic behaviors of the QCN system are presented, and the sufficient conditions for the stable QCN system are deduced. These sufficient conditions serve as guidelines toward proper parameters setting. Moreover, we find that the stability of QCN is mainly promised by the sliding mode motion, which is the underlying reason for QCN's stable queue shown in numerous simulations and experiments. Experiments on the NetFPGA platform verify that the analytical results can explain the complex behaviors of QCN.

Opportunistic Channel Scheduling for Ad Hoc Networks with Queue Stability

AbstractIn this paper, a distributed opportunistic channel access strategy in ad hoc network is proposed. We consider the multiple sources contend for the transmission opportunity, the winner source decides to transmit or restart contention based on the current channel condition. Owing to real data assumption at all links, the decision still needs to consider the stability of the queues. We formulate the channel opportunistic scheduling as a constrained optimization problem which maximizes the system average throughput with the constraints that the queues of all links are stable. The proposed optimization model is solved by Lyapunov stability in queueing theory. The successive channel access problem is decoupled into single optimal stopping problem at every frame and solved with

Stability and Capacity of Regular Information Theory and Communication Networks

The problem of throughput optimization in decentralized wireless networks with spatial randomness under queue stability and packet loss constraints are investigated. Two key performance measures are analyzed, namely the effective link throughput and the network spatial throughput. Specifically, the double of medium access probability, coding rate, and maximum number of retransmissions that maximize each throughput metric is analytically derived for a class of Poisson networks, in which packets arrive at the transmitters following a geometrical distribution. Necessary conditions so that the effective link throughput and the network spatial throughput are stable and achievable under bounded packet loss are determined, as well as upper bounds for both cases by considering the unconstrained optimization problem. It results show in which system configuration stable achievable throughput capacity obtained as a function of the network density and the arrival rate. Finally the time delay has get reduced at high rate. The throughput for the two nodes has been found efficiently.

Grid Power-Delay Tradeoff for Energy Harvesting Wireless Communication Systems with Finite Renewable Energy Storage

In this paper, we study the grid power-delay tradeoff in a point-to-point energy harvesting wireless communication system with finite energy storage capacity serving delay-sensitive applications. This communication system is powered by both grid and renewable power sources. First, we consider the average grid power consumption minimization subject to the data queue stability and renewable energy availability constraints. By exploring the optimality property and using the theory of random walks, we transform the grid power minimization problem to an asymptotically equivalent problem. Using the Lyapunov drift approach, we obtain an online dynamic power control policy to solve the asymptotically equivalent problem. Then, we introduce a novel analysis framework to study the grid power-delay tradeoff relationship of the online power control in the small delay regime. Specifically, using continuous-time approximation, dynamic programming and sample-path approach, we obtain bounds on the average delay and grid power consumption, which are asymptotically tight in the small delay regime. Based upon the derived closed-form expressions, we quantify the impacts of energy storage capacity and some other system parameters on the grid power-delay tradeoff.

Dynamic Compression-Transmission for Energy-Harvesting Multihop Networks With Correlated Sources

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data gathering protocol for multihop wireless sensor networks with energy harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable trade-off between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost. It is shown that the presence of side information can improve the network performance both in terms of overall network cost function and queue sizes.

Energy Management and Cross Layer Optimization for Wireless Sensor Network Powered by Heterogeneous Energy Sources

Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner to remain function for a potentially infinite lifetime. In this paper, we propose a new system model suitable for WSN, taking into account multiple energy consumptions due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multidimension stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-layer optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is applied to obtain a fully distributed and low-complexity cross-layer algorithm only requiring knowledge of the instantaneous system state. The explicit trade-off between the optimization objective and queue backlog is theoretically proven. Finally, the extensive simulations verify the theoretic claims.

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

We consider the problem of interference management and resource allocation in a cognitive radio network (CRNs) where the licensed spectrum holders (primary users) share their spare capacity with the non-licensed spectrum holders (secondary users). Under such shared spectrum usage the transmissions of the secondary users should have a minimal impact on the quality of service (QoS) and the operating conditions of the primary users. Therefore, it is important to distinguish the two types of users, and formulate the problem of resource allocation considering hard restrictions on the user-perceived QoS (such as packet end-to-end delay and loss) and physical-layer channel characteristics (such as noise and interference) of the primary users. To achieve this goal, we propose to assign the bandwidth and transmission power to minimize the total buffer occupancy in the system subject to capacity constraints, queue stability constraints, and interference requirements of the primary users. We apply this approach for resource allocation in a CRN built upon a Third Generation Partnership Project (3GPP) long-term evolution (LTE) standard platform. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.

Toward transcoding as a service: energy-efficient offloading policy for green mobile cloud

In this article we investigate energy-efficient offloading policy for transcoding as a service (TaaS) in a generic mobile cloud system. Computation on mobile devices can be offloaded to a mobile cloud system that consists of a dispatcher at the front end and a set of service engines at the back end. Particularly, a transcoding task can be executed on the mobile device (i.e. mobile execution) or offloaded and scheduled by the dispatcher to one of the service engines in the cloud (i.e. cloud execution). We aim to minimize the energy consumption of transcoding on the mobile device and service engines in the cloud while achieving low delay. For the mobile device, we formulate its offloading policy under delay deadline as a constrained optimization problem. We find an operational region on which execution mode, that is, mobile execution or cloud execution, is more energy efficient for the mobile device. For the cloud, we propose an online algorithm to dispatch transcoding tasks to service engines, with an objective to reduce energy consumption while achieving queue stability. By appropriately choosing the control variable, the proposed algorithm outperforms alternative algorithms, with lower time average energy consumption and time average queue length on the service engines. The proposed offloading policy can reduce energy consumption on both mobile devices and the cloud jointly, which provides guidelines for the design of green mobile cloud.

EcoPlan: energy-efficient downlink and uplink data transmission in mobile cloud computing

Mobile cloud computing (MC2) is emerging as a new computing paradigm that seeks to augment resource-constrained mobile devices for executing computing- and/or data-intensive mobile applications. Nonetheless, the energy-poverty nature of mobile devices has become a stumbling block that greatly impedes the practical application of MC2. Fortunately, for delay-tolerant mobile applications, energy conservation is achievable via two means: (1) dynamic selection of energy-efficient links (e.g., WiFi interface); and (2) deferring data transmission in bad connectivity. In this paper, we study the problem of energy-efficient downlink and uplink data transmission between mobile devices and clouds. In the presence of unpredictable data arrival, network availability and link quality, our objective is to minimize the time average energy consumption of a mobile device while ensuring the stability of both device-end and cloud-end queues. To achieve this goal, we propose an online control framework named EcoPlan under which mobile users can make flexible link selection and data transmission scheduling decisions to achieve arbitrary energy-delay tradeoffs. Real-world trace-driven simulations demonstrate the effectiveness of EcoPlan, along with its superior energy-efficiency over alternative WiFi-prioritized, minimum-delay and SALSA schemes.

Macrocell-Queue-Stabilization-Based Power Control of Femtocell Networks

We study the power control for femtocell networks in a heterogeneous spectrum-sharing network consisting of macrocells and femtocells. We propose a new approach that utilizes macrocell queue state information for femtocell power control. We formulate the underlying stochastic optimization problem to maximize average femtocell throughput under the constraint of macrocell queue stability by using the macrocell queue length information as a Lagrange weight of macrocell throughput. This formulation considers both cross-tier interference between femtocells and macrocells and intra-tier interference among femtocells. We design a centralized algorithm and a distributed algorithm for femtocells to decide downlink power for each sub-channel dynamically. Our simulation tests show that using local information, the distributed algorithm implementation can also achieve similar average femtocell throughput as the centralized algorithm by paying a penalty of larger macro queue length variation.