We investigate the performance of First-In, First-Out (FIFO) queues over wireless networks. We characterize the stability region of a general scenario where an arbitrary number of FIFO queues, which are served by a wireless medium, are shared by an arbitrary number of flows. In general, the stability region of this system is non-convex. Thus, we develop a convex inner-bound on the stability region, which is provably tight in certain cases. The convexity of the inner bound allows us to develop a resource allocation scheme; dFC. Based on the structure of dFC, we develop a stochastic flow control and scheduling algorithm; qFC. We show that qFC achieves optimal operating point in the convex inner bound. Simulation results show that our algorithms significantly improve the throughput of wireless networks with FIFO queues, as compared to the well-known queue-based flow control and max-weight scheduling.
In this work, our goal is to understand FIFO queues in wireless networks and develop efficient flow control and scheduling policies for such a setup. In the seminal paper @cite_21 , the authors analyze FIFO queues in an input queued switch. They show that the use of FIFO queues in that context limits the throughput to approximately 58 Backpressure routing and scheduling framework has emer-ged from the pioneering work @cite_16 @cite_17 , which has generated a lot of research interest @cite_0 ; especially for wireless ad-hoc networks @cite_3 @cite_7 @cite_23 @cite_2 @cite_20 @cite_6 . Furthermore, it has been shown that backpressure can be combined with flow control to provide utility-optimal operation guarantee @cite_10 @cite_20 . Such previous work mainly considered per-flow queues. However, FIFO queueing structure, which is the focus of this paper, is not compatible with the per-flow queueing requirements of these routing and scheduling schemes.
@cite_7 @cite_21 @cite_3 @cite_6 @cite_0 @cite_23 @cite_2 @cite_16 @cite_10 @cite_20 @cite_17
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It is well known that head-of-line blocking limits the throughput of an input-queued switch with first-in-first-out (FIFO) queues. Under certain conditions, the throughput can be shown to be limited to approximately 58.6 . It is also known that if non-FIFO queueing policies are used, the throughput can be increased. However, it has not been previously shown that if a suitable queueing policy and scheduling algorithm are used, then it is possible to achieve 100 throughput for all independent arrival processes. In this paper we prove this to be the case using a simple linear programming argument and quadratic Lyapunov function. In particular, we assume that each input maintains a separate FIFO queue for each output and that the switch is scheduled using a maximum weight bipartite matching algorithm. We introduce two maximum weight matching algorithms: longest queue first (LQF) and oldest cell first (OCF). Both algorithms achieve 100 throughput for all independent arrival processes. LQF favors queues with larger occupancy, ensuring that larger queues will eventually be served. However, we find that LQF can lead to the permanent starvation of short queues. OCF overcomes this limitation by favoring cells with large waiting times. There has been considerable work developing a stochastic network utility maximization framework using Backpressure algorithms, also known as MaxWeight. A key open problem has been the development of utility-optimal algorithms that are also delay-efficient. In this paper, we show that the Backpressure algorithm, when combined with the last-in-first-out (LIFO) queueing discipline (called LIFO-Backpressure), is able to achieve a utility that is within O(1 V) of the optimal value, for any scalar V ≥ 1, while maintaining an average delay of O([log(V)]2) for all but a tiny fraction of the network traffic. This result holds for a general class of problems with Markovian dynamics. Remarkably, the performance of LIFO-Backpressure can be achieved by simply changing the queueing discipline; it requires no other modifications of the original Backpressure algorithm. We validate the results through empirical measurements from a sensor network testbed, which show a good match between theory and practice. Because some packets may stay in the queues for a very long time under LIFO-Backpressure, we further develop the LIFOp-Backpressure algorithm, which generalizes LIFOp-Backpressure by allowing interleaving between first-in-first-out (FIFO) and LIFO. We show that LIFOp Backpressure also achieves the same O(1 V) close-to-optimal utility performance and guarantees an average delay of O([log(V)]2) for the packets that are served during the LIFO period. 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. We study a basic problem in Multi-Queue switches. A switch connects m input ports to a single output port. Each input port is equipped with an incoming FIFO queue with bounded capacity B. A switch serves its input queues by transmitting packets arriving at these queues, one packet per time unit. Since the arrival rate can be higher than the transmission rate and each queue has limited capacity, packet loss may occur as a result of insufficient queue space. The goal is to maximize the number of transmitted packets. This general scenario models most current networks (e.g., IP networks) which only support a “best effort” service in which all packet streams are treated equally. A 2-competitive algorithm for this problem was designed in [4] for arbitrary B. Recently, a ( 17 9 1.89 )-competitive algorithm was presented for B>1 in [2]. Our main result in this paper shows that for B which is not too small our algorithm can do better than 1.89, and approach a competitive ratio of ( e e-1 1.58 ). Backpressure routing and scheduling, with throughput-optimal operation guarantee, is a promising technique to improve throughput in wireless multi-hop networks. Although backpressure is conceptually viewed as layered, the decisions of routing and scheduling are made jointly, which imposes several challenges in practice. In this work, we present Diff-Max, an approach that separates routing and scheduling and has three strengths: (i) Diff-Max improves throughput significantly, (ii) the separation of routing and scheduling makes practical implementation easier by minimizing cross-layer operations; i.e., routing is implemented in the network layer and scheduling is implemented in the link layer, and (iii) the separation of routing and scheduling leads to modularity; i.e., routing and scheduling are independent modules in Diff-Max, and one can continue to operate even if the other does not. Our approach is grounded in a network utility maximization (NUM) formulation and its solution. Based on the structure of Diff-Max, we propose two practical schemes: Diff-subMax and wDiff-subMax. We demonstrate the benefits of our schemes through simulation in ns-2. We introduce a methodology for studying wireless ad hoc networks in a multihop traffic environment. Our approach is to use theoretical upper bounds on network performance for evaluating the effects of various design choices: we focus on power control, the queuing discipline, the choice of routing and media access protocols, and their interactions. Using this framework, we then concentrate on the problem of medium access for wireless multihop networks. We first study CSMA CA, and find that its performance strongly depends on the choice of the accompanying routing protocol. We then introduce two protocols that outperform CSMA CA, both in terms of energy efficiency and achievable throughput. The progressive back off algorithm (PBOA) performs medium access jointly with power control. The progressive rump up algorithm (PRUA) sacrifices energy efficiency in favor of higher throughput. Both protocols slot time, and are integrated with queuing disciplines that are more relaxed than the first in first out (FIFO) rule. They are totally distributed and the overhead they require does not increase with the size and node density of the network. In this paper we study the problem of jointly performing scheduling and congestion control in mobile ad-hoc networks so that network queues remain bounded and the resulting flow rates satisfy an associated network utility maximization problem. In recent years a number of papers have presented theoretical solutions to this problem that are based on combining differential-backlog scheduling algorithms with utility-based congestion control. However, this work typically does not address a number of issues such as how signaling should be performed and how the new algorithms interact with other wireless protocols. In this paper we address such issues. In particular: ldr We define a specific network utility maximization problem that we believe is appropriate for mobile adhoc networks. ldr We describe a wireless greedy primal dual (wGPD) algorithm for combined congestion control and scheduling that aims to solve this problem. ldr We show how the wGPD algorithm and its associated signaling can be implemented in practice with minimal disruption to existing wireless protocols. ldr We show via OPNET simulation that wGPD significantly outperforms standard protocols such as 802.11 operating in conjunction with TCP. This work was supported by the DARPA CBMANET program. This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multi-rate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with time-varying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dual-based algorithm remains stable and optimal when the constraint set is modulated by an irreducible finite-state Markov chain. This paper thus presents a step toward a systematic way to carry out cross-layer design in the framework of “layering as optimization decomposition” for time-varying channel models. Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling, or GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50 throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100 throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks, GMS achieves 100 throughput). This leads to a linear-time algorithm for identifying Local-Pooling-satisfying graphs. Moreover, by using similar graph-theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × n, GMS is guaranteed to achieve 66 throughput, thereby improving upon the previously known 50 lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies, its performance could be very bad. Overall, the paper demonstrates that using graph-theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms. We consider packet scheduling in a network providing differentiated services, where each packet is assigned a value. We study various queueing models for supporting QoS (Quality of Service). In the nonpreemptive model, packets accepted to the queue will be transmitted eventually and cannot be dropped. The FIFO preemptive model allows packets accepted to the queue to be preempted (dropped) prior to their departure, while ensuring that transmitted packets are sent in the order of arrival. In the bounded delay model, packets must be transmitted before a certain deadline, otherwise it is lost (while transmission ordering is allowed to be arbitrary). In all models the goal of the buffer policy is to maximize the total value of the accepted packets.Let α be the ratio between the maximal and minimal value. For the non-preemptive model we derive a Θ(log &alpha) competitive ratio, both exhibiting a buffer policy and a general lower bound. For the interesting case of two distinct values, we give an 2α--1 α competitive buffer policy, which exactly matches the lower bound. We also analyze a RED-like policy and derive its competitive ratio, which is approximately 2α--0.5 α for two values and Θ(log α) for multiple values. In addition we improve the previous known lower and upper bounds of the Fixed Partition and Flexible Partition policies.For the FIFO preemptive model, we improve the general lower bound and show a tight bound for the special case of queue size 2. We prove that the bounded delay model with uniform delay 2 is equivalent to a modified FIFO preemptive model with queue size 2. We then give improved upper and lower bounds on the 2-uniform bounded delay model. We also give lower bound for the 2-variable bounded delay model, which matches the previously known upper bound. A major challenge in the design and operation of wireless networks is to jointly route packets and schedule transmissions to efficiently share the common spectrum among links in the same area. Due to the lack of central control in wireless networks, these algorithms have to be decentralized. It was recently shown that distributed (greedy) algorithms can usually guarantee only fractional throughput. It was also recently shown that if a set of conditions regarding the network topology (known as Local Pooling) is satisfied, simple distributed maximal weight (greedy) scheduling algorithms achieve 100 throughput. In this paper, we focus on networks in which packets have to undergo multihop routing and derive multihop local pooling conditions for that setting. In networks satisfying these conditions, a backpressure-based joint routing and scheduling algorithm employing maximal weight scheduling achieves 100 throughput.