Fair Rate Allocation Research Articles

Optimization-Based Predictive Congestion Control for the Tor Network: Opportunities and Challenges

Based on the principle of onion routing, the Tor network achieves anonymity for its users by relaying user data over a series of intermediate relays. This approach makes congestion control in the network a challenging task. As of this writing, this results in higher latencies due to considerable backlog as well as unfair data rate allocation. In this article, we present a concept study of PredicTor, a novel approach to congestion control that tackles clogged overlay networks. Unlike traditional approaches, it is built upon the idea of distributed model predictive control, a recent advancement from the area of control theory. PredicTor is tailored to minimizing latency in the network and achieving max-min fairness. We contribute a thorough evaluation of its behavior in both toy scenarios to assess the optimizer and complex networks to assess its potential. For this, we conduct large-scale simulation studies and compare PredicTor to existing congestion control mechanisms in Tor. We show that PredicTor is highly effective in reducing latency and realizing fair rate allocations. In addition, we strive to bring the ideas of modern control theory to the networking community, enabling the development of improved, future congestion control. Thus, we demonstrate benefits and issues alike with this novel research direction.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

Adaptive Queue Management and Traffic Class Priority Based Fairness Rate Control in Wireless Sensor Networks

Congestion control in Wireless Sensor Networks (WSNs) is one of the key areas of research and different algorithms have been proposed using either of the notions of fair rate allocation, traffic class priority, and queue management. Use of the any one of the above is not adequate to address the challenges. Hence, in this paper, we have proposed a novel congestion control algorithm using the combined notions of fair allocation of bandwidth, prioritizing traffic classes, and Adaptive Queue Management (ADQM). The proposed Weighted Priority based Fair Queue Gradient Rate Control (WPFQGRC) scheme achieves the fair distribution of spare bandwidth by considering the traffic class priority, average queue size, and the connected loads of a node. Average queue size at every node is adapted based on the proposed notion of the gradient of the differential of Global Priority (GP) with respect to the differential of queue size. The output rate of a given node is computed based on the GP of the node and the average queue size. The spare bandwidth of a node is fairly distributed by taking into account the connected load of the given node. The proposed algorithm is developed to suit to a general topology of WSN, however for the sake of illustration, we have considered a tree topology network that deals with both Real-Time (RT) and Non-Real Time (NRT) traffic classes. The proposed algorithm is implemented in NS3 platform in Linux environment and the performance of the algorithm is evaluated in terms of throughput, packet loss, packet delay, traffic class patterns, node mobility, and the average queue size. The performance of the proposed algorithm is found to be superior to that of Yaghmaee et al. ’s, Brahma et al. ’s, Monowar et al. ’s, Sarode et al. ’s, Difference of Differentials Rate Control (DDRC), Weighted Priority based DDRC (WPDDRC), and Priority based Fairness Rate Control (PFRC) algorithms respectively.

Optimal Link Scheduling in Millimeter Wave Multi-Hop Networks With MU-MIMO Radios

This paper studies the maximum throughput achievable with optimal scheduling in multi-hop mmWave picocellular networks with Multi-user Multiple-Input Multiple-Output (MU-MIMO) radios. MU-MIMO enables simultaneous transmission to multiple receivers (Space Division Multiplexing) and simultaneous reception from multiple transmitters (Space Division Multiple Access). The main contribution is the extension to MU-MIMO of the Network Utility Maximization (NUM) scheduling framework for multi-hop networks. We generalize to MU-MIMO the classic proof that Maximum Back Pressure (MBP) scheduling is NUM optimal. MBP requires the solution of an optimization that becomes harder with MU-MIMO radios. In prior models with one-to-one transmission and reception, each valid schedule was a matching over a graph. However, with MU-MIMO each valid schedule is, instead, a Directed Bipartite SubGraph (DBSG). In the general case this prevents finding efficient algorithms to solve the scheduler. We make MU-MIMO MBP scheduling tractable by assuming fixed power allocation, so the optimal scheduler is the Maximum Weighted DBSG. The MWDBSG problem can be solved using standard Mixed Integer Linear Programing. We simulate multi-hop mmWave picocellular networks and show that a MU-MIMO MBP scheduler enables a 160% increase in network throughput versus the classic one-to-one MBP scheduler, while fair rate allocation mechanisms are used in both cases.

Fair and Efficient Rate Allocation for Wireless-Powered Machine-Type Communication Networks

This paper proposes a bargaining game theoretic rate allocation scheme for wireless-powered machine-type communications (MTCs). In the considered body area MTC network (MTCN), a battery-powered user equipment (UE) acting as the MTC gateway (MTCG) is responsible for collecting the information uploaded by in/on body wireless-powered MTC devices (MTCDs). By solving the Nash bargaining solution (NBS) of the proposed cooperative game, the minimum rate requirements of the MTCDs are satisfied. In addition, the network resource can be allocated to the MTCDs in a fair and efficient manner regarding the difference of their channel qualities. In comparison to other traditional resource allocation methods, the simulation results show that the proposed NBS-based method obtains a good tradeoff between the system efficiency and per-node fairness.

Hierarchical Beamforming

We consider hierarchical beamforming in wireless networks. For a given population of flows, we propose computationally efficient algorithms for fair rate allocation including proportional fairness and max-min fairness. We further propose closed-form formulas for flow level performance, for both elastic and streaming traffic (1).

Hierarchical beamforming: Resource allocation, fairness and flow level performance

We consider hierarchical beamforming in wireless networks. For a given population of flows, we propose computationally efficient algorithms for fair rate allocation including proportional fairness and max–min fairness. We next propose closed-form formulas for flow level performance, for both elastic (with either proportional fairness and max–min fairness) and streaming traffic. We further assess the performance of hierarchical beamforming using numerical experiments. Since the proposed solutions have low complexity compared to conventional beamforming, our work suggests that hierarchical beamforming is a promising candidate for the implementation of beamforming in future cellular networks.

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this work, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fair rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. We further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.

Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this paper, we show that mixing scheduled and random access incurs an inherent throughput/delay cost and the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.

NOMA-Based Multi-Pair Two-Way Relay Networks With Rate Splitting and Group Decoding

In this paper, we develop a non-orthogonal multiple access (NOMA)-based multi-pair two-way relay (TWR) network, in which a rate splitting scheme and a successive group decoding strategy are employed. By exploiting the interference signals received from neighbor users with the leverage of the full-duplex technique, we enhance the decoding ability of each user and further achieve an effective multiuser interference management for the network. We propose different decoding strategies for different types of nodes by processing the received signals with only local incoming channel state information in different manners. Moreover, under the limited group decoding size, each individual node decodes its own desired messages along with a fraction of the interference successively. We further investigate the joint uplink and downlink fair rate allocation problem based on the max–min criterion, and the solution to which also contains the optimal group decoding schedule. Simulation results in terms of ergodic rate and outrage probability corroborate the superiority of our NOMA-based multi-pair TWR network over the OMA-based counterpart.

Utility Fair Rate Allocation in LTE/802.11 Networks

We consider proportional fair rate allocation in a heterogeneous network with a mix of LTE and 802.11 cells which supports multipath and multihomed operation (simultaneous connection of a user device to multiple LTE BSs and 802.11 APs). We show that the utility fair optimisation problem is non-convex but that a global optimum can be found by solving a sequence of convex optimisations in a distributed fashion. The result is a principled approach to offload from LTE to 802.11 and for exploiting LTE/802.11 path diversity to meet user traffic demands.

A Novel Flow-Aware Fair Scheduler for Multi Transmit/Receive Wireless Networks

This paper considers the problem of deriving a time-division multiple-access (TDMA) schedule for multi-hop wireless networks that allow nodes to perform multiple transmissions/receptions to/from all of their neighbors simultaneously over the same frequency. To date, there are a number of link schedulers for the networks in question but they do not consider flow rate or any notion of fairness when deriving a TDMA schedule. Henceforth, we address this critical gap by proposing a link scheduler, called Algo-Fair, that, in addition to maximizing the number of links in each slot, also provides fairness among flows. In addition, it uses a novel augmentation step to distribute spare capacity fairly among flows. We believe this step is general and can be applied readily in other forms of wireless networks. Apart from that, Algo-Fair generates a schedule directly while yielding a fair rate allocation. This is different from existing methods that first use a flow contention graph to compute a fair share before deriving the corresponding schedule, which may not exist. Numerical results show Algo-Fair has higher fairness, minimum rate, and average total throughput than competing approaches, and low end-to-end delay.

Max-min Fair Rate Allocation and Routing in Energy Harvesting Networks: Algorithmic Analysis

This paper considers max-min fair rate allocation and routing in energy harvesting networks where fairness is required among both the nodes and the time slots. Unlike most previous work on fairness, we focus on multihop topologies and consider different routing methods. We assume a predictable energy profile and focus on the design of efficient and optimal algorithms that can serve as benchmarks for distributed and approximate algorithms. We first develop an algorithm that obtains a max-min fair rate assignment for any routing that is specified at the input. We then turn to the problem of determining a “good” routing. For time-invariable unsplittable routing, we develop an algorithm that finds routes that maximize the minimum rate assigned to any node in any slot. For fractional routing, we derive a combinatorial algorithm for the time-invariable case with constant rates. We show that the time-variable case is at least as hard as the 2-commodity feasible flow problem and design an FPTAS to combat the high running time. Finally, we show that finding an unsplittable routing or a routing tree that provides lexicographically maximum rate assignment (i.e., the best in the max-min fairness terms) is NP-hard, even for a time horizon of a single slot. Our analysis provides insights into the problem structure and can be applied to other related fairness problems.

Fair Congestion Control in Vehicular Networks With Beaconing Rate Adaptation at Multiple Transmit Powers

Cooperative intervehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, which are called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, which is called channel congestion due to beaconing activity. In this paper, we propose to let vehicles transmit with different transmit power levels, each with a particular beaconing rate. The selection of the rate is modeled as a network utility maximization (NUM) problem. Our goal is to maximize the number of beacons delivered at each transmit power according to a well-defined fairness notion while complying with the maximum allowed beaconing load on the channel. The algorithm parameters can be set per vehicle and dynamically changed, which provides enough flexibility to support multiple applications on top of the control scheme, whereas the NUM model provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. Simulation results validate our approach and show that it provides fair rate allocations in realistic multihop and dynamic scenarios with packet losses.

Distributed and Fair Beaconing Rate Adaptation for Congestion Control in Vehicular Networks

Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally apply the notion of fairness of a beaconing rate allocation in vehicular networks and to control the trade-off between efficiency and fairness. The NUM methodology provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. In this context, we focus exclusively in beaconing rate control and propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. The desired fairness notion in the allocation can be established with an algorithm parameter. Simulation results validate our approach and show that FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios.

A stable bio-inspired resource assignment strategy for elastic traffic

Nature-inspired methods have attracted many attentions today for solving difficult and non-linear engineering problems. Some important examples include gravitational search algorithm (GSA), genetic algorithm (GA), DNA computing, artificial immune systems, fuzzy logic etc. Fair network resource allocation strategies are based on solving a form of constrained optimisation problem. There are plenty of high-speed fair rate allocation methods in the literature, some of them are based on fuzzy controllers for improving the convergence speed and are not necessarily optimal. Hence, in the current research, some recent important heuristics such as GA, GSA and their fuzzy-based counterparts have been used for finding the optimal allocated rates. Stability analysis is presented to guarantee the convergence property of the algorithm. After simulating, the results show that using the mixed fuzzy-GSA approach improves the conventional methods in convergence speed and results in fewer oscillations.

Proportional Fair MU-MIMO in 802.11 WLANs

We consider the proportional fair rate allocation in an 802.11 WLAN that supports multi-user MIMO (MU-MIMO) transmission by one or more stations. We characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities. While a number of features carry over from the case without MU-MIMO, in general neither flows nor stations need to be allocated equal airtime when MU-MIMO is available.

Bounds to Fair Rate Allocation and Communication Strategies in Source/Relay Wireless Networks

We analyze the achievable data rate of cooperative relaying strategies in networks where nodes operate in half-duplex mode. Nodes have to deliver their data to a gateway, at a certain rate, and may have limited energy capabilities, as in the case of energy-harvesting communication networks. Both the requested data rate and the available energy capabilities may vary from node to node. Under such constraints, we take an information-theoretic approach and derive cut-set upper bounds to the achievable rate. Furthermore, we devise two kinds of communication strategies, each aiming at a different objective. The former ensures a fair rate allocation to the network nodes, but it neglects their energy constraints. The latter does consider energy constraints by meeting the requirements on the average power consumption at each node and by providing fairness in the data rate allocation. We show the performance of the aforementioned communication strategies, highlighting their effectiveness and providing useful insights on the system behavior.

Max-min fair rate allocation for VCP in multi-bottleneck networks

Variable-structure congestion Control Protocol VCP leverages the existing two Explicit Congestion Notification ECN bits for network congestion feedback and achieves comparable performance to eXplicit congestion Control Protocol XCP. VCP utilises the encoded load factor-based feedback scheme to deliver congestion signals. In multi-bottleneck networks, all bottleneck links on the transmission path would contribute to the aggregate congestion signal, and VCP fails to achieve max-min fairness. To address this problem, a new metric called overload period is introduced to measure the congestion level of links, and the overload period-based feedback scheme is proposed to make sure that each VCP flow's rate is controlled only by the link with minimum overload period encountered on the transmission path. It is proved that the link with minimum overload period is the flow's bottleneck, and the max-min fair rate allocation is achieved in multi-bottleneck networks. The overload period-based feedback scheme is validated using NS2 simulations.

Monitoring Quality Maximization through Fair Rate Allocation in Harvesting Sensor Networks

In this paper, we consider an energy harvesting sensor network where sensors are powered by reusable energy such as solar energy, wind energy, and so on, from their surroundings. We first formulate a novel monitoring quality maximization problem that aims to maximize the quality, rather than the quantity, of collected data, by incorporating spatial data correlation among sensors. An optimization framework consisting of dynamic rate weight assignment, fair data rate allocation, and flow routing for the problem is proposed. To fairly allocate sensors with optimal data rates and efficiently route sensing data to the sink, we then introduce a weighted, fair data rate allocation and flow routing problem, subject to energy budgets of sensors. Unlike the most existing work that formulated the similar problem as a linear programming (LP) and solved the LP, we develop fast approximation algorithms with provable approximation ratios through exploiting the combinatorial property of the problem. A distributed implementation of the proposed algorithm is also developed. The key ingredients in the design of algorithms include a dynamic rate weight assignment and a reduction technique to reduce the problem to a special maximum weighted concurrent flow problem, where all source nodes share the common destination. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is very promising, and the solution to the weighted, fair data rate allocation and flow routing problem is fractional of the optimum.