This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are provided to illustrate the cost of approaching optimality. This theory is also applicable to problems in operations research and economics, where energy-efficient and profit-maximizing decisions must be made without knowing the future. Topics in the text include the following: - Queue stability theory - Backpressure, max-weight, and virtual queue methods - Primal-dual methods for non-convex stochastic utility maximization - Universal scheduling theory for arbitrary sample paths - Approximate and randomized scheduling theory - Optimization of renewal systems and Markov decision systems Detailed examples and numerous problem set questions are provided to reinforce the main concepts. Table of Contents: Introduction / Introduction to Queues / Dynamic Scheduling Example / Optimizing Time Averages / Optimizing Functions of Time Averages / Approximate Scheduling / Optimization of Renewal Systems / Conclusions

Stochastic Network Optimization with Application to Communication and Queueing Systems

(1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

https://link.springer.com/content/pdf/10.1057%2Fjors.1987.184.pdf

Applied Probability and Queues

Current data collection protocols for wireless sensor networks are mostly based on quasi-static minimum-cost routing trees. We consider an alternative, highly-agile approach called backpressure routing, in which routing and forwarding decisions are made on a per-packet basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average end-to-end packet delays for delivered packets drastically (75% under high load, 98% under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60% improvement in max-min rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks.

/pdf/routing-without-routes-the-backpressure-collection-protocol-1ak0dr1fnf.pdf

Routing without routes: the backpressure collection protocol

Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (eg, vehicle speed and location or traffic signal state and timing) Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver This paper also extends the convergence analysis for two important cases, ie, measurement noise in the input signal and delay in the update process This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios

LIMERIC: A Linear Adaptive Message Rate Algorithm for DSRC Congestion Control

Back-pressure-type algorithms based on the algorithm by Tassiulas and Ephremides have recently received much attention for jointly routing and scheduling over multihop wireless networks. However, this approach has a significant weakness in routing because the traditional back-pressure algorithm explores and exploits all feasible paths between each source and destination. While this extensive exploration is essential in order to maintain stability when the network is heavily loaded, under light or moderate loads, packets may be sent over unnecessarily long routes, and the algorithm could be very inefficient in terms of end-to-end delay and routing convergence times. This paper proposes a new routing/scheduling back-pressure algorithm that not only guarantees network stability (throughput optimality), but also adaptively selects a set of optimal routes based on shortest-path information in order to minimize average path lengths between each source and destination pair. Our results indicate that under the traditional back-pressure algorithm, the end-to-end packet delay first decreases and then increases as a function of the network load (arrival rate). This surprising low-load behavior is explained due to the fact that the traditional back-pressure algorithm exploits all paths (including very long ones) even when the traffic load is light. On the other-hand, the proposed algorithm adaptively selects a set of routes according to the traffic load so that long paths are used only when necessary, thus resulting in much smaller end-to-end packet delays as compared to the traditional back-pressure algorithm .

https://users.ece.utexas.edu/~shakkott/Pubs/sp-bp.pdf

On combining shortest-path and back-pressure routing over multihop wireless networks

The IEEE 802.11p standard specifies the PHY and MAC layer operations for transmitting and receiving periodic broadcast messages for vehicular safety. Many studies have identified issues with the CSMA based IEEE 802.11p MAC at high densities of devices, mainly reflected by low packet reception rate. In this paper, we make an interesting observation that with increasing density, the IEEE 802.11p MAC tends towards an ALOHA-type behavior where concurrent transmissions by close-by devices are not prevented. This behavior can lead to poor packet reception rate even for vehicles in close neighborhood. Many efforts have been made to address the IEEE 802.11p MAC issues to provide better performance for DSRC safety applications, including the introduction of Decentralized Congestion Control (DCC) algorithm to ETSI standards in Europe. In this paper, we evaluate the performance of the proposed DCC algorithm and observe that the nominal parameters in DCC are unsuitable in many scenarios. Using transmit power control as an example, we develop a simple rule within the DCC framework that can significantly improve the safety packet reception performance with increasing densities. The DCC algorithms are fully compatible with the IEEE 802.11p standards and asynchronous in nature. A parallel approach to handle high device densities is a slotted synchronous MAC, where time is slotted based on GPS synchronization and each transmitter contends for a set of recurring time slots (or channels) with periodicity matching the required safety message periodicity. As compared to the per-packet based contention scheme as in CSMA defined in IEEE 802.11, such a scheme is much better suited for periodic safety broadcast. In this paper, we design a standard compliant TDM overlay on top of the MAC layer that can significantly improve the packet reception performance. Combined with a distributed resource selection protocol, the synchronous MAC can discover even more neighboring devices than the improved asynchronous approach, making DSRC safety applications more reliable.

http://csi.dgist.ac.kr/uploads/Seminar/1408_CCV_JBM.pdf

Congestion control for vehicular safety: synchronous and asynchronous MAC algorithms

The past few years have seen increasing interest in understanding social networks as a medium for community interaction. A major challenge has been to understand various fundamental properties of social networks that form the basis for the formation and propagation of opinions across such networks. The main hurdle has been the absence of plausible models that specify the correlations between different members of a social network, which could then be used for algorithm design. This paper studies an influence maximization problem using an Ising-model-based approach. We first validate the credibility of the ferromagnetic Ising model in predicting opinion formation in social networks using cosponsorship data from the US Senate proceedings. We then develop a greedy placement algorithm that can efficiently find an appropriate subset of network members, “bribing” whom can efficiently propagate a particular opinion in the network. We use simulations to confirm the effectiveness of the greedy placement algorithm.

http://inlab.lab.asu.edu/Publications/LiuYinSha_10.pdf

Influence maximization in social networks: An ising-model-based approach

We consider multiuser scheduling in wireless networks with channel variations and flow-level dynamics. Recently, it has been shown that the MaxWeight algorithm, which is throughput-optimal in networks with a fixed number of users, fails to achieve the maximum throughput in the presence of flow-level dynamics. In this paper, we propose a new algorithm, called Workload-based Scheduling with Learning, which is provably throughput-optimal, requires no prior knowledge of channels and user demands, and performs significantly better than previously suggested algorithms.

http://inlab.lab.asu.edu/Publications/LiuYinSri_11.pdf

Throughput-optimal opportunistic scheduling in the presence of flow-level dynamics

The back-pressure algorithm introduced in 1992 by Tassiulas and Ephremides is a well-known distributed and adaptive routing/scheduling algorithm where nodes only need the queue-length information of neighboring nodes to make routing decisions. Packets are adaptively routed in the network according to congestion information, which makes the algorithm resilient to traffic and topology changes. However, the back-pressure algorithm requires routers to maintain a separate queue for each destination, which precludes its implementation in large-scale networks. In this paper, we propose a distributed cluster-based back-pressure routing algorithm that retains the adaptability of back-pressure routing while significantly reducing the number of queues that have to be maintained at each node.

http://inlab.lab.asu.edu/Publications/YinSriTow_11.pdf

Shihuan Liu

Papers

On combining shortest-path and back-pressure routing over multihop wireless networks

Congestion control for vehicular safety: synchronous and asynchronous MAC algorithms

Influence maximization in social networks: An ising-model-based approach

Throughput-optimal opportunistic scheduling in the presence of flow-level dynamics

Cluster-based back-pressure routing algorithm