This paper focuses on congestion control over multihop, wireless networks. In a wireless network, an important constraint that arises is that due to the MAC (Media Access Control) layer. Many wireless MACs use a time-division strategy for channel access, where, at any point in space, the physical channel can be accessed by a single user at each instant of time. In this paper, we develop a fair hop-by-hop congestion control algorithm with the MAC constraint being imposed in the form of a channel access time constraint, using an optimization based framework. In the absence of delay, we show that this algorithm are globally stable using a Lyapunov function based approach. Next, in the presence of delay, we show that the hop-by-hop control algorithm has the property of spatial spreading. In other words, focused loads at a particular spatial location in the network get "smoothed" over space. We derive bounds on the "peak load" at a node, both with hop-by-hop control, as well as with end-to-end control, show that significant gains are to be had with the hop-by-hop scheme, and validate the analytical results with simulation.

We consider scheduling policies for maxmin fair allocation of bandwidth in wireless adhoc networks. We formalize the maxmin fair objective under wireless scheduling constraints. We propose a fair scheduling which assigns dynamic weights to the flows such that the weights depend on the congestion in the neighborhood and schedule the flows which constitute a maximum weighted matching. It is possible to analytically prove that this policy attains both short term and long term fairness. We consider more generalized fairness notions, and suggest mechanisms to attain these objectives. I.

We consider the ring network with spatial reuse. Traffic streams may enter and exit the network at any node. We adopt an arrival traffic model with deterministic constraints on its sample paths, which conforms to the output traffic of a leaky bucket rate control mechanism. A transmission policy specifies at each time which traffic stream will be transmitted at the outgoing link by each node. We provide an upper bound on the asymptotic backlog of the ring that holds for all work-conserving policies and is independent of the initial conditions. This bound remains finite as long as the maximum load of every link is less than one. The latter condition is also necessary for the existence of an asymptotic bound that is independent of the initial conditions.

Sharing the locally common spectrum among the links of the same vicinity is a fundamental problem in wireless ad-hoc networks. Lately some scheduling approaches have been proposed that guarantee fair share of the bandwidth among the links. What really affects the quality of service perceived by the applications though, is the effective end-to-end bandwidth allocated to the different network sessions that span several links of such a wireless adhoc network. In the current paper we propose an algorithm that provides fair session rates in that context. The algorithm is based on a combination of a link scheduling method to avoid local conflicts, a fair session service discipline per link and a hop-by-hop window flow control scheme. It is proven in the paper that the long term rates allocated to the different sessions are maxmin fair. All the stages of the algorithm are implementable based on local information only, except of the link scheduling part that needs some networkwide coordination of the links. When the latter is replaced by an approximate distributed link scheduling algorithm then we may have a fully distributed solution with suboptimal performance. Some numerical study is performed to evaluate the impact of various parameter choices on the performance of the algorithm.

We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.

We propose a new multicast routing and scheduling algorithm called multipurpose multicast routing and scheduling algorithm (MMRS). The routing policy load balances amongst various possible routes between the source and the destinations, basing its decisions on the message queue lengths at the source node. The scheduling amongst various sessions sharing links is devised such that the flow of a session depends on the congestion of the next hop links. MMRS is throughput optimal and computationally simple. It can be implemented in a distributed, asynchronous manner. It has several parameters which can be suitably modified to control the end to end delay, packet loss in a topology specific manner. These parameters can be adjusted to offer limited priorities to some desired sessions. MMRS is expected to play a significant role in end to end congestion control in the multicast scenario.

It is shown that stability of the celebrated MaxWeight or back pressure policies is a consequence of the following interpretation: either policy is myopic with respect to a surrogate value function of a very special form, in which the “marginal disutility ” at a buffer vanishes for vanishingly small buffer population. This observation motivates the h-MaxWeight policy, defined for a wide class of functions h. These policies that share many of the attractive properties of the MaxWeight policy: (i) The policy does not require arrival rate data. (ii) The h-myopic policy is stabilizing when h is a perturbation of a monotone linear function, or a monotone Lyapunov function for the fluid model. (iii) A perturbation of the relative value function for a workload relaxation gives rise to a myopic policy that is approximately average-cost optimal in heavy traffic, with logarithmic regret. The first results are obtained for a completely general stochastic network model. Asymptotic optimality is established for the general scheduling model with a single bottleneck.

We consider the problem of uplink/downlink scheduling in a multichannel wireless access point network where channel states differ across channels as well as users, vary with time, and can be measured only infrequently. We demonstrate that, unlike infrequent measurement of queue lengths, infrequent measurement of channel states reduce the maximum attainable throughput. We then prove that in frequency division multiplexed systems, a dynamic scheduling policy that depends on both the channel rates (averaged over the measurement interval) and the queue lengths, attains the maximum possible throughput. We also generalize the scheduling policy to solve the joint power allocation and scheduling problem in orthogonal frequency division multiplexed systems. In addition, we provide simulation studies that demonstrate the impact of the frequency of channel and queue state measurements on the average delay and attained throughput. Index Terms Throughput-optimal scheduling, Multichannel access point networks, Infrequent channel measurements.

Scheduling in an input buffered switch can be viewed as repeated matching (corresponding to once every time slot) in a bipartite graph. It has been shown that scheduling algorithms based on maximum weight matching (MWM) with queue-lengths as the weights, leads to excellent performance in terms of throughput and delay. However, computing MWM using a strongly polynomial time algorithm requires O(n 3) operations in an n×n switch. The main motivation for this paper comes from the following two observations: (1) The weights of edges (packets in buffer) change only a little between successive time slots, thus changing the weight of the MWM only by a small amount; (2) Under MWM algorithm, the average queue-sizes are small. The main difficulty in utilizing these properties comes from the fact that small changes in weights can change the matching arbitrarily, thus making it hard for current popular algorithms to compute an MWM quickly using the information from past (or memory). In this paper, we develop an algorithm based on the algorithm of Cunningham and Marsh [1] that uses the above two properties in order to to find the new MWM quickly. Specifically, for an n port input-queued switch, i.e. a switch with n inputs and n outputs, our algorithm finds MWM in O(n²) operations using past information. We believe that the incremental nature of our algorithm may be useful in the context of other applications.

A major challenge for today's manufacturing organizations is how to respond agilely and cost-effectively to dynamic fluctuations of demand patterns across product mix and increasing rates of new products introduction. As traditional process planning and production scheduling operate independently for process plans and production schedules generation respectively, production schedules generation processes become inflexible and is restricted by the pregenerated process plan. In response to this issue, a multi-agent Monte Carlo configuration and optimization method is proposed to integrate process planning, production scheduling, and resource optimization for concurrent evaluation of planning and scheduling options. The principle of this methodology is to integrate agent bidding mechanism and Monte Carlo optimization, in which agents perform iterative interactions to evaluate production decisions dynamically for an optimum solution determination.