An optimized bandwidth allocation in broadband digital economy

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow, as it can contribute more than 50 percent of the job completion time. While several coflow schedulers have been proposed, we observe that routing, as a factor greatly impacting the Coflow Completion Time (CCT), has not been well considered. In this article, we focus on the coflow scheduling problem and jointly consider routing and bandwidth allocation. We begin by providing an analytical solution to the problem of optimal bandwidth allocation with pre-determined routes. In the following, we formulate the problem of scheduling a single coflow as a Non-linear Mixed Integer Programming problem and present its relaxed convex optimization problem. We further propose two algorithms, CoRBA and its simplified version: CoRBA-fast that solve the single coflow scheduling problem with a joint consideration of routing and bandwidth allocation. Lastly, to address multiple coflows in online scheduling, we propose an online scheduler named OnCoRBA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that CoRBA and CoRBA-fast reduce the CCT by 30-400 percent and the OnCoRBA scheduler reduces the average online CCT by 20-230 percent. In addition, CoRBA-fast can be hundreds times faster than CoRBA with around 8 percent performance degradation compared to CoRBA, which makes the use of CoRBA-fast very appropriate in practice.

Smart camera networks (SCNs) are increasingly used in applications such as homeland security, border control and traffic monitoring. The cameras are often wireless with an organically growing structure, to reduce the overhead of deployment. We frame a new and important problem in SCNs: how to transmit videos from multiple cameras with overlapping coverage given the limited available wireless bandwidth to maximize the quality of the received videos. We call this problem Coordinated Transport of Correlated Videos (CTCV). CTCV is a more general version of 3D video transport: in that problem, highly correlated videos from two cameras (that provide the 3D perspective) are jointly encoded exploiting their pre-defined and known overlap. In contrast, in CTCV there is an arbitrary number of cameras whose overlap is not known apriori and that require transmission as multiple video streams. To effectively support CTCV, we propose a video delivery protocol that consists of two primary components: (1) Consolidation of correlated videos from multiple cameras which removes spatially redundant fields-of-view; and (2) Network and coverage aware bandwidth allocation to optimize coverage quality cooperatively among the different video streams to match the available bandwidth. We formulate the problem of optimal bandwidth allocation for maximizing coverage. We propose and investigate different heuristic policies for bandwidth allocation. We evaluate CTCV using data from a small camera testbed as well as topologies from realistic deployments. Experiments show that CTCV achieves around 10 dB gains in video quality in the scenarios we consider.

All-optical networks have been largely investigated due to their high data transmission rates. In the traditional Wavelength-Division Multiplexing (WDM) technology, the spectrum of light that can be transmitted through the optical fiber has been divided into frequency intervals of fixed width, with a gap of unused frequencies between them. Recently, an alternative emerging architecture was suggested which moves away from the rigid Dense WDM (DWDM) model towards a flexible model, where usable frequency intervals are of variable width (even within the same link). Each light path has to be assigned a frequency interval (sub-spectrum), which remains fixed through all of the links it traverses. Two different light paths using the same link must be assigned disjoint sub-spectra. This technology is termed flex-grid (or, flex-spectrum), as opposed to fixed-grid (or, fixed-spectrum) current technology. In this work we study a problem of optimal bandwidth allocation arising in the flex-grid technology. In this setting, each light path has a lower and upper bound on the width of its frequency interval, as well as an associated profit, and we want to find a bandwidth assignment that maximizes the total profit. This problem is known to be NP-Complete. We observe that, in fact, the problem is inapproximable within any constant ratio even on a path network. We further derive NP-hardness results and present approximation algorithms for several special cases of the path and ring networks, which are of practical interest. Finally, while in general our problem is hard to approximate, we show that an optimal solution can be obtained by allowing resource augmentation. Our study has applications also in real time scheduling.

For a fixed total bandwidth expansion factor, and for a fixed channel code rate, we consider the problem of optimal bandwidth allocation between the source coder and the spread-spectrum unit for a multicarrier direct-sequence CDMA system operating over a frequency-selective fading channel with narrowband interference. Assuming a Gaussian source with the optimum scalar quantizer and a binary convolutional code with soft-decision decoding, we obtain both a lower and an upper bound on the end-to-end average source distortion. The optimal bandwidth allocation is then numerically computed by minimizing upper and lower bounds on the average distortion. We show that the upper bound based cost function is a convex function of the source code rate, and the optimal allocation depends on the system and the channel conditions, such as the total number of active users, the number of carriers, and the average jammer-to-signal power ratio.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values.

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for (1) the maximization of the sum capacity of all users; (2) the maximization of the worst user capacity; and (3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results.

The growing popularity for video based applications has put an enormous strain on the network and causes the network to be more prone to congestion. The study of congestion control and bandwidth allocation problems is often formulated into Network Utility Maximization (NUM) framework, and the existing solutions for NUM generally focus on single-layered applications. However, today's quality of video is divided into several layers, where each layer provides different level of enhancement of quality. In this paper, we study how multi-layered video based applications impact network performance and pricing through NUM formulation, in particularly traffic from video streaming. In our investigation, we design and implement a new multi-layered user utility model that leverages on studies of human visual perception. Then, using this new utility model to examine network activities, we demonstrate that solving NUM with multi-layered utility is intractable, and that rate allocation and network pricing may oscillate due to user behavior specific to multi-layered applications. To address this, we propose a new approach for admission control to ensure quality of service and experience.

This letter investigates the performance bounds of sensing and communications (S&C) in perceptive networks, where multiple user equipments (UEs) are deployed with both S&C capabilities whose performances are both closely related to the allocated bandwidth. To this end, we develop a novel bandwidth allocation strategy which optimizes the weighted average range resolution for sensing, while guaranteeing the sum-rate among UEs for wireless communications. We first formulate the bandwidth allocation problem into a convex optimization programming, and then develop a highly efficient algorithm to obtain the optimal solution. Through a deeper analysis of the problem structure, we build an initial understanding and provide insights into the performance tradeoff between S&C, which can be achieved by three specifically tailored bandwidth allocation schemes. Numerical results are given to verify the performance of the proposed method, showing that our bandwidth allocation scheme achieves the pareto boundary of S&C performance, with significantly reduced computational complexity.

In this paper, a priority-based bandwidth allocation in the context of heterogeneous wireless network is studied. By utilizing the mobile terminals with multi-homing capability, which simultaneously accesses bandwidth from different wireless network, a novel utility-based bandwidth allocation approach is introduced. An essential factor is the priority mechanism considering the services priority classification, which means the higher-priority service request can be guaranteed on bandwidth allocation prior to the lower-priority service request. The utility function is used to distinguish both the service priority and the network subscribers compared to other users. The bandwidth allocation problem is formulated as an optimization problem and solved by Lagrangian duality. Numerical results reveal that the optimal bandwidth allocation algorithm can differentiates the priority-based service and utilize all available resources within the network capacity.

To satisfy the stringent requirement of capacity enhancement in wireless networks, cooperative relaying is envisioned as one of the most effective solutions. In this paper, we focus on the problem of capacity enhancement by way of relay stations (RSs) placement, which is a critical task of network planning and deployment to achieve an efficient and scalable network design. To fully exploit the performance benefits of cooperative relaying, we develop an optimization framework to maximize the capacity as well as meet the minimal traffic demand for each subscriber station (SS). The problem of joint RS placement and bandwidth allocation is formulated into a mixed integer nonlinear program and solved through a heuristic approach based on genetic algorithm (GA). Moreover, an upper bound on the capacity is derived to assist the estimation of system performance given a network configuration. Numerical results are presented to demonstrate the effectiveness of the solution approach and the performance benefits due to RS placement and optimal bandwidth allocation through cooperative relaying.

Bandwidth allocation and admission control scheme based on Nash bargaining solution for WiMAX systems

Content caching at edge of the network has been considered as an effective way to reduce latency and alleviate burden on backhaul. In this paper, cache placement and bandwidth allocation strategies are jointly investigated in a multi-user system of a heterogenous network consisting of one macro base station and multiple small base stations. With the goal of minimizing outage probability, a simulated annealing algorithm-aided cache placement scheme along with an optimized bandwidth allocation via a dual decomposition method is proposed while taking into account the constraints of total available bandwidth, storage capacity of caching nodes, and fairness among users. Unlike previous works, the optimizations of the cache placement and bandwidth allocation are jointly considered in this paper. Meanwhile, in order to guarantee performance of each individual user while optimizing outage probability of the entire system, user fairness is taken into account as well. Simulation results show that, compared with other cache placement and bandwidth allocation schemes, the proposed method can obtain lower outage probability and maintain user fairness.

One of the most important features of the evolving Fourth Generation (4G) wireless communication system is heterogeneous wireless access in which users could connect to several wireless access networks simultaneously. The new feature brings new challenges for the bandwidth allocation (BA) among heterogeneous networks. A non-cooperative bandwidth allocation game (NCBAG) algorithm for heterogeneous wireless networks is proposed in this paper. A BA problem is modeled as a non-cooperative game, and formulated to maximize the total utility of different networks. The existence of Nash equilibrium is verified for the proposed game model. The utility functions are developed for different applications to avoid assigning too much bandwidth to single user. Simulation results show that our proposed scheme can not only achieve high utility, but also reduce the blocking probability within a few steps of iteration.

A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)–based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze its asymptotic detection performance. Subsequently, we optimize the sensor-wise quantization thresholds to achieve near-optimal asymptotic performance at the local sensor level. Moreover, we propose a mixed-integer linear programming approach to solve the optimization problem of transmission bandwidth allocation accounting for bandwidth constraints and error-prone channels. Finally, simulation results demonstrate the superiority of the proposed hybrid detector and the bandwidth allocation strategy, especially in challenging error-prone channel conditions.