Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks

Ji, Bo; Joo, Changhee; Shroff, Ness B.

doi:10.1109/tnet.2012.2227790

Cited by 97 publications

(66 citation statements)

References 35 publications

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“…In this section we give a brief overview of SPNs, and we show that the multi-hop switched queueing network and the Back-Pressure policy described above are special cases of the SPN model and the Maximum Pressure policy studied in [20], respectively. The reason we do this is two-fold: (i) all fluid models of multi-hop switched queueing networks under the Back-Pressure policy that have appeared thus far assume fixed routing [17,37,38,43]. By appealing to the very broad modeling class of SPNs we are able to extract a concrete fluid model …”

Section: Lemmamentioning

confidence: 99%

“…Finally, there is growing literature on fluid models of the Max-Weight and Back-Pressure policies in a variety of settings, e.g., single-hop and multi-hop switched queueing networks, as well as stochastic processing networks [17,20,37,38,43,57]. Although the present paper employs very similar fluid models and in that sense builds on these prior works, our objective is quite different: fluid approximations have been used in existing literature in order to prove stability of the corresponding queueing networks or state-space collapse phenomena under critical loads, while in the present paper to facilitate a delay analysis in the presence heavy-tailed traffic.…”

Section: Introductionmentioning

confidence: 99%

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Delay Analysis of the Max-Weight Policy Under Heavy-Tailed Traffic via Fluid Approximations

2018

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We consider switched queueing networks with a mix of heavy-tailed (i.e., arrival processes with infinite variance) and exponential-type traffic and study the delay performance of the MaxWeight policy, known for its throughput optimality and asymptotic delay optimality properties. Our focus is on the impact of heavy-tailed traffic on exponential-type queues/flows, which may manifest itself in the form of subtle rate-dependent phenomena. We introduce a novel class of Lyapunov functions (piecewise linear and nonincreasing in the length of heavy-tailed queues) whose drift analysis, combined with results in [31], can provide exponentially decaying upper bounds to queue-length tail asymptotics despite the presence of heavy tails. To facilitate a drift analysis we employ fluid approximations, proving that if a continuous and piecewise linear function is also a "Lyapunov function" for the fluid model, then the same function is a "Lyapunov function" for the original stochastic system. Furthermore, we use fluid approximations and renewal theory in order to prove delay instability results, i.e., infinite expected delays in steady state. We illustrate the benefits of the proposed approach in two ways: (i) analytically, by studying the delay stability regions of single-hop switched queueing networks with disjoint schedules, providing a precise characterization of these regions for certain queues and inner and outer bounds for the rest. As a side result, we prove monotonicity properties for the service rates of different schedules that, in turn, allow us to identify "critical configurations" towards which the state of the system is driven, and which determine to a large extent delay stability; (ii) computationally, through a Bottleneck Identification algorithm, which identifies (some) delay unstable queues/flows in complex switched queueing networks by solving the fluid model from certain initial conditions.

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Section: Lemmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delay Analysis of the Max-Weight Policy Under Heavy-Tailed Traffic via Fluid Approximations

2018

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“…Several approaches have been proposed to solve the delay problem of backpressure algorithms [10][11][12][13][14]. Instead of using queue differentials as weights of the MaxWeight problem, [11] proposes representing weights with delay information of packets in the queues.…”

Section: A Backpressure Algorithmsmentioning

confidence: 99%

“…Instead of using queue differentials as weights of the MaxWeight problem, [11] proposes representing weights with delay information of packets in the queues. The idea is that packets that have already experienced high delays are more likely to be scheduled for transmission in the next time slot, whereas the original backpressure algorithm would give longer queues higher priority irrespective of the delay experienced by packets.…”

Section: A Backpressure Algorithmsmentioning

confidence: 99%

Channel Sensitivity of LIFO-Backpressure: Quirks and Improvements

Starobinski

Hashemi

et al. 2016

IEEE Trans. Control Netw. Syst.

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Abstract-We study the delay performance of backpressure routing algorithms using LIFO schedulers (LIFO-backpressure). We uncover a surprising behavior in which, under certain channel conditions, the average delay of packets is high at low traffic load and decreases as the load in the network increases. We propose and analyze a queueing-theoretic model under which the scheduler can transmit packets only if the queue length meets or exceeds a threshold, and we show that the model analytically bears out the observed phenomenon. Using matrix geometric methods, we derive a numerical solution for the average packet delay in the general case, and, using z-transform techniques, we further provide closed-form solutions for a special case. Our analysis indicates that when the threshold is fixed (as may happen under lossless channel conditions), the average delay is small at low traffic load and increases with increasing load, as expected. On the other hand, when the threshold fluctuates (as may happen under changing, lossy channel conditions), the average delay may be high at low load and decrease, sometimes substantially, with the traffic load. We corroborate these findings with TOSSIM simulations on different types of networks, using measured channel traces. Further, we propose a replicationbased LIFO-backpressure algorithm (RBL) to improve the delay performance of LIFO-backpressure. Analytical and simulation results show that RBL dramatically reduces the delay of LIFObackpressure at low load, while maintaining high throughput performance at high load.

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“…Yet, both the time-varying nature of wireless channels and the scheduling policy significantly affect the regularity of the received data of each mobile user. The traditional scheduling policies aiming to maximize the system throughput (e.g., [10], [17], and [22]) or provide various fairness guarantees (e.g., [3], [16], [18], and [21]) at the base station side do not take users' experience into account and thus lead to the high irregularity of the received data of mobile users.…”

mentioning

confidence: 99%

Wireless Scheduling Design for Optimizing Both Service Regularity and Mean Delay in Heavy-Traffic Regimes

Li²,

Eryılmaz

2016

IEEE/ACM Trans. Networking

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We consider the design of throughput-optimal scheduling policies in multihop wireless networks that also possess good mean delay performance and provide regular service for all links-critical metrics for real-time applications. To that end, we study a parametric class of maximum-weight-type scheduling policies, called Regular Service Guarantee (RSG) Algorithm, where each link weight consists of its own queue length and a counter that tracks the time since the last service, namely Time-Since-Last-Service (TSLS). The RSG Algorithm not only is throughput-optimal, but also achieves a tradeoff between the service regularity performance and the mean delay, i.e., the service regularity performance of the RSG Algorithm improves at the cost of increasing mean delay. This motivates us to investigate whether satisfactory service regularity and low mean-delay can be simultaneously achieved by the RSG Algorithm by carefully selecting its design parameter. To that end, we perform a novel Lyapunov-drift-based analysis of the steady-state behavior of the stochastic network. Our analysis reveals that the RSG Algorithm can minimize the total mean queue length to establish mean delay optimality under heavily loaded conditions as long as the design parameter weighting for the TSLS scales no faster than the order of , where measures the closeness of the network load to the boundary of the capacity region. To the best of our knowledge, this is the first work that provides regular service to all links while also achieving heavy-traffic optimality in mean queue lengths.Index Terms-Heavy-traffic analysis, mean delay, service regularity, throughput, wireless scheduling. 1063-6692

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Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks

Cited by 97 publications

References 35 publications

Delay Analysis of the Max-Weight Policy Under Heavy-Tailed Traffic via Fluid Approximations

Delay Analysis of the Max-Weight Policy Under Heavy-Tailed Traffic via Fluid Approximations

Channel Sensitivity of LIFO-Backpressure: Quirks and Improvements

Wireless Scheduling Design for Optimizing Both Service Regularity and Mean Delay in Heavy-Traffic Regimes

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