Backpressure Algorithm Research Articles

Delay-Aware Optimization Framework for Proportional Flow Delay Differentiation in Millimeter-Wave Backhaul Cellular Networks

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

Delay-Optimal Back-Pressure Routing Algorithm for Multihop Wireless Networks

Although the back-pressure (BP) algorithm has been proven to be a throughput-optimal policy for traffic scheduling and routing in wireless networks, the optimal control of delays in the BP algorithm remains an open problem, especially for conventional multihop networks. To enhance the delay performance, we proposed an improved BP algorithm called sojourn-time-based BP (STBP) by introducing a novel delay metric called the sojourn time backlog (STB). The STB considers the queue length and accumulated packet delays comprehensively. It provides more pressure to push forward flows suffering from greater delays. Based on this new metric, the calculation of the routing weight for each packet is determined for maximizing the difference of the STB in the routing process. The proposed routing algorithm is robust and distributed, and does not require any prior knowledge of network connections and load conditions. We analyze the network stability with delay considerations and prove the throughput optimality of the STBP in multihop networks. Simulation results reveal that the enhanced algorithm effectively improves the end-to-end delay while ensuring throughput optimality.

A novel implementation of backpressure algorithm in wireless ad hoc network

In the wireless networks, the routing technique is the one of the highest concern and it is the important procedure in the ad hoc networks. To aid this effort, we proposed a new valuation of backpressure appliances for wireless networks. By this proposed system, we will address numerous preparation and routing difficulties and also recover the throughput and delay that are essentially produced by the packets at the node transmission. The Backpressure routing is a dense and enlarged throughput for wireless networks, but endures improved delays. In routing, the backpressure algorithm is known to afford throughput optimality with active traffic. The significant supposition in the backpressure algorithm is that all nodes are kind and detect the algorithm rules leading the information conversation and principal optimization necessities. In the proposed system, we validate that how the node is steady at the backpressure algorithm routing and also by together easing the virtual trust line and the real package queue. The backpressure algorithm not only achieves flexibility, but also stands the throughput performance under safety attacks. This scheme is mostly enhances the node performance at the time of announcement and also it recovers the node security at the time of numerous threats in the wireless requests.

Loop-Free Backpressure Routing Using Link-Reversal Algorithms

The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand. For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm and via simulation show that its delay performance is better than that of backpressure.

Throughput-Optimal Multi-Hop Broadcast Algorithms

We design throughput-optimal dynamic broadcast algorithms for multi-hop networks with arbitrary topologies. Most of the previous broadcast algorithms route packets along spanning trees. For large time-varying networks, computing and maintaining a set of spanning trees is not efficient, as the network-topology may change frequently. In this paper, we design a class of dynamic algorithms, which make simple packet-by-packet scheduling and routing decisions, and hence obviate the need for maintaining any global topological structures, such as spanning trees. Our algorithms may be conveniently understood as a non-trivial generalization of the familiar back-pressure algorithm for unicast traffic, which performs packet routing and scheduling based on queue lengths. However, in the broadcast setting, due to packet duplications, it is difficult to define appropriate queuing structures. We design and prove the optimality of a virtual queue-based algorithm, where virtual queues are defined for subsets of nodes. We then propose a multi-class broadcast policy, which combines the above scheduling algorithm with in-class-in-order packet forwarding, resulting in significant reduction in complexity. Finally, we evaluate the performance of the proposed algorithms via extensive numerical simulations.

Improvement of ZigBee Using by Thread and Backpressure Algorithm

Improvement of ZigBee Using by Thread and Backpressure Algorithm

Routing Optimization with Efficient Second Order Distributed Approach Using Congestion Control Rules

Distributed joint congestion control and routing optimization has received a significant amount of attention recently. To date, however, most of the existing schemes follow a key idea called the back-pressure algorithm. Despite having many salient features, the first-order sub gradient nature of the back-pressure based schemes results in slow convergence and poor delay performance. To overcome these limitations, the present study was made as first attempt at developing a second-order joint congestion control and routing optimization framework that offers utility-optimality, queue-stability, fast convergence, and low delay. Contributions in this project are three-fold. The present study propose a new second-order joint congestion control and routing framework based on a primal-dual interior-point approach and established utility-optimality and queue-stability of the proposed second-order method. The results of present study showed that how to implement the proposed second-order method in a distributed fashion.

High-Throughput Network Coding Aware Routing in Time-Varying Multihop Networks

Network coding aware routing (NCAR) has been emerging as an effective approach to create coding opportunities actively in multihop networks. To the best of our knowledge, most of the existing methods focus on a “static” design that considers coding opportunities based on fixed routes and invariable packet flows. This “static” way results in the optimality of routes suffering from a high sensitive behavior on both network topology and flow dynamic and, hence, reduces the throughput of routes in time-varying networks. To solve this problem, we propose a high-throughput NCAR scheme that supports dynamic multihop network connectivity and achieves throughput optimization, even with multiple variable flows. In the proposed scheme, a back-pressure-based NCAR algorithm is designed to push the flows dynamically into the nodes that have better network coding opportunities with no knowledge of network topology and flow dynamic. As a byproduct, the proposed method also exhibits a significantly improved delay performance relative to the traditional back-pressure algorithm. Simulation results reveal that the proposed scheme improves network capacity and alleviates end-to-end delay in heavy load networks.

Securing the Backpressure Algorithm for Wireless Networks

The backpressure algorithm is known to provide throughput optimality in routing and scheduling decisions for multi-hop networks with dynamic traffic. The essential assumption in the backpressure algorithm is that all nodes are benign and obey the algorithm rules governing the information exchange and underlying optimization needs. Nonetheless, such an assumption does not always hold in realistic scenarios, especially in the presence of security attacks with intent to disrupt network operations. In this paper, we propose a novel mechanism, called virtual trust queuing , to protect backpressure algorithm based routing and scheduling protocols against various insider threats. Our objective is not to design yet another trust-based routing to heuristically bargain security and performance, but to develop a generic solution with strong guarantees of attack resilience and throughput performance in the backpressure algorithm. To this end, we quantify a node's algorithm-compliance behavior over time and construct a virtual trust queue that maintains deviations of a give node from expected algorithm outcomes. We show that by jointly stabilizing the virtual trust queue and the real packet queue, the backpressure algorithm not only achieves resilience, but also sustains the throughput performance under an extensive set of security attacks. Our proposed solution clears a major barrier for practical deployment of backpressure algorithm for secure wireless applications.

Adaptive Resource Balancing for Serviceability Maximization in Fog Radio Access Networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria toward a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In this paper, we propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) are balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH mobilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low-RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared with the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput.

English

English

Diversity Backpressure Scheduling and Routing With Mutual Information Accumulation in Wireless Ad-Hoc Networks

We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple packet streams (commodities) when only the average channel state information is present and that only locally. The proposed algorithms are modifications of the Diversity Backpressure (DIVBAR) algorithm, under which the packet whose commodity has the largest "backpressure metric" is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially received packet in a separate "partial packet queue", thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-RMIA, under which all the receiving nodes clear the received partial information of a packet once one or more receiving nodes firstly decode the packet; and DIVBAR-MIA, under which all the receiving nodes retain the partial information of a packet until the packet has reached its destination. We characterize the network capacity region with RMIA and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimum among the polices with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR on the achievable throughput. Moreover, we prove that DIVBAR-MIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.

Back-Pressure Traffic Signal Control With Fixed and Adaptive Routing for Urban Vehicular Networks

City-wide control and coordination of traffic flow can improve efficiency, fuel consumption, and safety. We consider the problem of controlling traffic lights under fixed and adaptive routing of vehicles in urban road networks. Multicommodity back-pressure algorithms, originally developed for routing and scheduling in communication networks, are applied to road networks to control traffic lights and adaptively reroute vehicles. The performance of the algorithms is analyzed using a microscopic traffic simulator. The results demonstrate that the proposed multicommodity and adaptive routing algorithms provide significant improvement over a fixed schedule controller and a single-commodity back-pressure controller in terms of various performance metrics, including queue length, trips completed, travel times, and fair traffic distribution.

On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks

In energy harvesting wireless sensor networks, energy imbalance among sensor nodes is detrimental to network performance and battery life. Particularly, nodes that are closer to a data sink or have less energy replenishment tend to exhaust the energy earlier, leading to some sub-regions of the environment being left unmonitored. Existing research efforts focus on the energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. With the consideration of the unpredictability of the harvestable energy, in this article, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make routing decision, which could help mitigate the energy imbalance problem. We develop two online policies: (1) Energy-balanced Backpressure Routing Algorithm for lossless networks and (2) Enhanced Energy-balanced Backpressure Routing Algorithm for time varying wireless networks with lossy links. Both Energy-balanced Backpressure Routing Algorithm and Enhanced Energy-balanced Backpressure Routing Algorithm are distributed, queuing stable, and do not require the explicit knowledge of the statistics of the energy harvesting. The simulation data show that our developed algorithms can achieve significantly higher performance in terms of energy balance than existing schemes such as Original Backpressure Algorithm and the Backpressure Collection Protocol.

When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction.

Towards a throughput-optimal routing algorithm for data collection on satellite networks

To perform Earth observation, more and more satellites are equipped with high-resolution sensors like hyperspectral imagers, which generate data at tens of Gbps. Researchers aim to maximize the volume of data available for use on the ground in near-real time. To achieve this goal, many solutions have recently been proposed to perform data collection through satellite networks. Most of these solutions have so far been focusing on either source rate control and load balancing, or heuristic routing. However, optimizing routing together with resources allocation is critical for improving delivery performance. In this paper, we challenge the fact that transmission paths have to be built under high link variability with limited resources, and develop a throughput-optimal solution based on the utility maximization framework. For delay performance, we embed a distance factor into the objective of the framework and derive a geographic-location-aware backpressure algorithm. Further, we exploit transmission opportunities missed by backpressure-type algorithms to accelerate data transfer. The simulation results show that our algorithms are able to deliver large volumes of data in time, and scale well in all scenarios tested.

Joint Congestion Control and Routing Optimization: An Efficient Second-Order Distributed Approach

Distributed joint congestion control and routing optimization has received a significant amount of attention recently. To date, however, most of the existing schemes follow a key idea called the back-pressure algorithm. Despite having many salient features, the first-order subgradient nature of the back-pressure based schemes results in slow convergence and poor delay performance. To overcome these limitations, in this paper, we make a first attempt at developing a second-order joint congestion control and routing optimization framework that offers utility-optimality, queue-stability, fast convergence, and low delay. Our contributions in this paper are three-fold: i) we propose a new second-order joint congestion control and routing framework based on a primal-dual interior-point approach; ii) we establish utility-optimality and queue-stability of the proposed second-order method; and iii) we show how to implement the proposed second-order method in a distributed fashion.

Resource Allocation With Video Traffic Prediction in Cloud-Based Space Systems

This paper considers the resource allocation problems for video transmission in space-based information networks. The queueing system analyzed in this study is constituted by multiple users and a single server. The server is operated as a cloud that can sense the traffic arrivals to each user's queue and then allocates the transmission resource and service rate for users. The objectives are to make configurations over time to minimize the time average cost of the system, and to minimize the waiting time of packets after they enter the queue. Meanwhile, the constraints on the queue stability of the system must be satisfied. In this paper, we introduce a predictive backpressure algorithm, which considers the future arrivals with a certain prediction window size into the consideration of resource allocation to make decisions on which packets to be served first. In addition, this paper designs a multiresolution wavelet decomposition-based backpropagation network for the prediction of video traffic, which exhibits the long-range dependence property. Simulation results indicate that the delay of the queueing system can be reduced through this prediction-based resource allocation, and the prediction accuracy for the video traffic is improved according to the proposed prediction system.

Enhancing the Delay Performance of Dynamic Backpressure Algorithms

For general multi-hop queueing networks, delay optimal network control has unfortunately been an outstanding problem. The dynamic backpressure (BP) algorithm elegantly achieves throughput optimality, but does not yield good delay performance in general. In this paper, we obtain an asymptotically delay optimal control policy, which resembles the BP algorithm in basing resource allocation and routing on a backpressure calculation, but differs from the BP algorithm in the form of the backpressure calculation employed. The difference suggests a possible reason for the unsatisfactory delay performance of the BP algorithm, i.e., the myopic nature of the BP control. Motivated by this new connection, we introduce a new class of enhanced backpressure-based algorithms which incorporate a general queue-dependent bias function into the backpressure term of the traditional BP algorithm to improve delay performance. These enhanced algorithms exploit queue state information beyond one hop. We prove the throughput optimality and characterize the utility-delay tradeoff of the enhanced algorithms. We further focus on two specific distributed algorithms within this class, which have demonstrably improved delay performance as well as acceptable implementation complexity.

Design of Scheduling Algorithms for End-to-End Backlog Minimization in Wireless Multi-Hop Networks Under -Hop Interference Models

In this paper, we study the problem of link scheduling for multi-hop wireless networks with per-flow delay constraints under the $K$ -hop interference model. Specifically, we are interested in algorithms that maximize the asymptotic decay-rate of the probability with which the maximum end-to-end backlog among all flows exceeds a threshold, as the threshold becomes large. We provide both positive and negative results in this direction. By minimizing the drift of the maximum end-to-end backlog in the converge-cast on a tree, we design an algorithm, Largest-Weight-First (LWF), that achieves the optimal asymptotic decay-rate for the overflow probability of the maximum end-to-end backlog as the threshold becomes large. However, such a drift minimization algorithm may not exist for general networks. We provide an example in which no algorithm can minimize the drift of the maximum end-to-end backlog. Finally, we simulate the LWF algorithm together with a well known algorithm (the back-pressure algorithm) and a large-deviations optimal algorithm in terms of the sum-queue (the P-TREE algorithm) in converge-cast networks. Our simulation shows that our algorithm performs significantly better not only in terms of asymptotic decay-rate, but also in terms of the actual overflow probability.