TCP-Aware Backpressure Routing and Scheduling

In this work, we explore the performance of backpressure routing and scheduling for TCP flows over wireless networks. TCP and backpressure are not compatible due to a mismatch between the congestion control mechanism of TCP and the queue size based routing and scheduling of the backpressure framework. We propose a TCP-aware backpressure routing and scheduling that takes into account the behavior of TCP flows. TCP-aware backpressure (i) provides throughput optimality guarantees in the Lyapunov optimization framework, (ii) gracefully combines TCP and backpressure without making any changes to the TCP protocol, (iii) improves the throughput of TCP flows significantly, and (iv) provides fairness across competing TCP flows

Accelerated Backpressure Algorithm

We develop an Accelerated Back Pressure (ABP) algorithm using Accelerated Dual Descent (ADD), a distributed approximate Newton-like algorithm that only uses local information. Our construction is based on writing the backpressure algorithm as the solution to a network feasibility problem solved via stochastic dual subgradient descent. We apply stochastic ADD in place of the stochastic gradient descent algorithm. We prove that the ABP algorithm guarantees stable queues. Our numerical experiments demonstrate a significant improvement in convergence rate, especially when the packet arrival statistics vary over time.Comment: 9 pages, 4 figures. A version of this work with significantly extended proofs is being submitted for journal publicatio

Effective Scheduling for Coded Distributed Storage in Wireless Sensor Networks

A distributed storage approach is proposed to access data reliably and to cope with node failures in wireless sensor networks. This approach is based on random linear network coding in combination with a scheduling algorithm based on backpressure. Upper bounds are provided on the maximum rate at which data can be reliably stored. Moreover, it is shown that the backpressure algorithm allows to operate the network in a decentralized fashion for any rate below this maximum

When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction

Investigation into the effect of backpressure on the mechanical behavior of methane-hydrate-bearing sediments via DEM analyses

Backpressure has been extensively applied in experimental tests to improve the water saturation of samples, and its effect on the strength of saturated soils has been traditionally regarded as trivial in Soil Mechanics. However, a non-negligible influence of backpressure on the macro mechanical properties of methane-hydrate-bearing-sediments (MHBS) has been surprisingly observed in several recent experiments reported in the literature. This paper aims to shed light on this phenomenon. A theoretical analysis on the microscopic interaction between soil grains and inter-particle methane hydrate (MH) was carried out to highlight how backpressure affects the mechanical properties of the inter-particle MH which in turn affect the macroscopic mechanical behavior of MHBS. The influence of backpressure is accounted for in a new bond contact model implemented into the Distinct Element Method (DEM). Then, a series of DEM biaxial compression tests were run to investigate the link between mechanical properties of MHBS and backpressure. The DEM numerical results show that shear strength, small strain stiffness and shear dilation of MHBS increase with the level of backpressure. As the critical state is approached, the influence of backpressure ceases. Moreover, the elastic modulus and cohesion of MHBS increase linearly while the internal friction angle decreases at a decreasing rate as the backpressure increases. Simple analytical relationships were achieved so that the effect of backpressure on the mechanical properties of MHBS can be accounted in the design of laboratory tests to characterize the mechanical behavior of MHBS