Load Balancing via Random Local Search in Closed and Open systems

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.Comment: Accepted to Sigmetrics 201

Joint-optimal probing and scheduling in wireless systems

Consider a wireless system where a sender can transmit data to various users with independent and varying channel conditions. To maximize its long-term transmission rate, the sender should always transmit to the user with the best channel. To discover which user has the best channel, it has to spend time to probe channels, and this reduces the time available for effective transmission. This paper aims at identifying optimal joint probing and scheduling strategies. These strategies realize the best trade-off between the channel state acquisition and effective transmission. We first provide general structural properties of optimal strategies, and then exactly characterize these strategies in particular but relevant cases. Finally we propose extensions of this problem, e.g., to impose fairness among the users, we investigate how to maximize system utility rather than throughput

Adaptive network coding and scheduling for maximizing throughput in wireless networks

Recently, network coding emerged as a promising technology that can provide significant improvements in throughput and energy efficiency of wireless networks, even for unicast communication. Often, network coding schemes are designed as an autonomous layer, independent of the underlying Phy and MAC capabilities and algorithms. Consequently, these schemes are greedy, in the sense that all opportunities of broadcasting combinations of packets are exploited. We demonstrate that this greedy design principle may in fact reduce the network throughput. This begets the need for adaptive network coding schemes. We further show that designing appropriate MAC scheduling algorithms is critical for achieving the throughput gains expected, from network coding. In this paper, we propose a general framework to develop optimal and adaptive joint network coding and scheduling schemes. Optimality is shown for various Phy and MAC constraints. We apply this framework to two different network coding architectures: COPE, a scheme recently proposed in [7], and XOR-Sym, a new scheme we present here. XOR-Sym is designed to achieve a lower implementation complexity than that of COPE, and yet to provide similar throughput gains

Optimal Distributed Scheduling in Wireless Networks Under the SINR Interference Model

In wireless networks, the design of radio resource sharing mechanisms is complicated by the complex interference constraints among the various links. In their seminal paper (IEEE Trans. Autom. Control, vol. 37, no. 12, pp. 1936-1948), Tassiulas and Ephremides introduced Maximum Weighted Scheduling, a centralized resource sharing algorithm, and proved its optimality. Since then, there have been extensive research efforts to devise distributed implementations of this algorithm. Recently, distributed adaptive CSMA scheduling schemes have been proposed and shown to be optimal, without the need of message passing among transmitters. However, their analysis relies on the assumption that interference can be accurately modeled by a simple interference graph. In this paper, we consider the more realistic and challenging signal-to-interference-plus-noise ratio (SINR) interference model. We present distributed scheduling algorithms that: 1) are optimal under the SINR interference model; and 2) do not require any message passing. These algorithms are based on a combination of a simple and efficient power allocation strategy referred to as Power Packing and randomization techniques. The optimality of our algorithms is illustrated in various traffic scenarios using numerical experiments

Rate adaptation games in wireless LANs : Nash equilibrium and price of anarchy

In Wireless LANs, users may adapt their transmission rates depending on the radio conditions of their links so as to maximize their throughput. Recently, there has been a significant research effort in developing distributed rate adaptation schemes. Unlike previous works that mainly focus on channel tracking, this paper characterizes the optimal reaction of a rate adaptation protocol to the contention information received from the MAC. We formulate this problem analytically. We study both competitive and cooperative user behaviors. In the case of competition, users selfishly adapt their rates so as to maximize their own throughput, whereas in the case of cooperation they adapt their rates so as to maximize the overall system throughput. We show that the Nash Equilibrium reached in the case of competition is inefficient (i.e. the price of anarchy goes to infinity as the number of users increases), and provide insightful properties of the socially optimal rate adaptation schemes. We find that recently proposed collision-aware rate adaptation algorithms decrease the price of anarchy. We also propose a novel collision-aware rate adaptation algorithm that further reduces the price of anarchy

Scheduling with limited information in wireless systems

Opportunistic scheduling is a key mechanism for improving the performance of wireless systems. However, this mechanism requires that transmitters are aware of channel conditions (or CSI, Channel State Information) to the various possible receivers. CSI is not automatically available at the transmitters, rather it has to be acquired. Acquiring CSI consumes resources, and only the remaining resources can be used for actual data transmissions. We explore the resulting trade-off between acquiring CSI and exploiting channel diversity to the various receivers. Specifically, we consider a system consisting of a transmitter and a fixed number of receivers/users. An infinite buffer is associated to each receiver, and packets arrive in this buffer according to some stochastic process with fixed intensity. We study the impact of limited channel information on the stability of the system. We characterize its stability region, and show that an adaptive queue length-based policy can achieve stability whenever doing so is possible. We formulate a Markov Decision Process problem to characterize this queue length-based policy. In certain specific and yet relevant cases, we explicitly compute the optimal policy. In general case, we provide a scheduling policy that achieves a fixed fraction of the system's stability region. Scheduling with limited information is a problem that naturally arises in cognitive radio systems, and our results can be used in these systems

Mobility-driven Scheduling in Wireless Networks

Abstract—The design of scheduling policies for wireless data systems has been driven by a compromise between the objectives of high overall system throughput and the degree of fairness among users, while exploiting multi-user diversity, i.e., fast-fading variations. These policies have been thoroughly investigated in the absence of user mobility, i.e., without slow fading variations. In the present paper, we examine the impact of intra- and inter-cell user mobility on the trade-off between throughput and fairness, and on the suitable choice of α-fair scheduling policies. We consider a dynamic setting where users come and go over time as governed by random finite-size data transfers, and explicitly allow for users to roam around. It is demonstrated that the overall performance improves as the fairness parameter α is reduced, and in particular, that proportional fair scheduling may yield relatively poor performance, in sharp contrast to the standard scenario with only fast fading. Since a lower α tends to affect short-term fairness, we explore how to set the fairness parameter so as to strike the right balance between overall performance and short-term fairness. It is further established that mobility tends to improve the performance, even when the network operates under a local fair scheduling policy as opposed to a globally optimal strategy. We present extensive simulation results to confirm and illustrate the analytical findings. I