Counter-intuitive throughput behaviors in networks under end-to-end control

In multi-rate wireless LANs, throughput-based fair bandwidth allocation can lead to drastically reduced aggregate throughput. To balance aggregate throughput while serving users in a fair manner, proportional fair or time-based fair scheduling has been proposed to apply at each access point (AP). However, since a realistic deployment of wireless LANs can consist of a network of APs, this paper considers proportional fairness in this much wider setting. Our technique is to intelligently associate users with APs to achieve optimal proportional fairness in a network of APs. We propose two approximation algorithms for periodical offline optimization. Our algorithms are the first approximation algorithms in the literature with a tight worst-case guarantee for the NP-hard problem. Our simulation results demonstrate that our algorithms can obtain an aggregate throughput which can be as much as 2.3 times more than that of the max-min fair allocation in 802.11b. While maintaining aggregate throughput, our approximation algorithms outperform the default user-AP association method in the 802.11b standard significantly in terms of fairness.

The problem how to determine the capacity of and achieve fairness in mesh networks is one of the key topics in practical and theoretical research on mesh networks. Max-min fairness is one way to define fairness and several algorithms how to compute max-min fair rate allocations are already published. In this paper we make two major contributions to this area of research: First, we formulate an algorithm achieving max-min fairness among end-to-end flows based on the effective load of a collision domain. This allows us to determine max-min fair rate allocations in a multi-gateway, multi-channel mesh network with equal rates for all links. Second, we extend this algorithm for heterogeneous link rates.

We consider a centralized spectrum server that coordinates the transmissions of a group of links sharing a common spectrum. Links employ on-off modulation with fixed transmit power when active. In the on state, a link obtains a data rate determined by the signal-to-interference ratio on the link. By knowing the link gains in the network, the spectrum server finds an optimal schedule that maximizes the average sum rate subject to a minimum average rate constraint for each link. Using a graph theoretic model for the network and a linear programming formulation, the resulting schedules are a collection of transmission modes (sets of active links) that are time shared in a fashion that is reminiscent of spatial reuse patterns in cellular networks. In the special case when there is no minimum rate constraint, the optimal schedule results in a fixed dominant mode with highest sum rate being operated all the time. In order to offset the inherent unfairness in the above solution, we introduce a minimum rate constraint and characterize the resulting loss in sum rate when compared to the case when there is no minimum rate constraint. We also investigate alternate fairness criteria by designing scheduling algorithms that achieve max-min fairness and proportional fairness. It is shown that the max-min fair rate allocation maximizes the minimum common rate among the links. Simulation results are presented and future work is described

Heterogeneous network (HetNet) is a promising solution to meeting the unprecedented demand for higher data rate in the next generation mobile networks. Compared with the traditional single-layer cellular networks, the resource management (e.g., user association and power control problems) is more challenging in the multi-tier HetNets. In addition, due to the new application scenarios and battery-limited nature of the user equipment (UE), improving energy efficiency (EE) of individual while ensuring EE fairness among UEs is one of the key design issues in the uplink HetNets. In this paper, by considering the fairness for EE, joint user association and power control problems are studied in the uplink multiple input multiple output (MIMO) enabled HetNets. Under the quality of service (QoS) and transmit power constraints of UE, two kinds of fairness, namely, proportional fairness and max-min fairness, are investigated. The optimization problems of maximizing the EE are formulated by considering the fairness among UEs. Considering the non-convex characteristic of the problem, the formulated problem is divided into two subproblems, i.e., user association and power control subproblems, then an iterative algorithm is proposed to achieve the fair EE resource allocation for each fairness criterion until convergence. For the proportional fairness problem, the dual decomposition and Newton methods are applied. Then, by using successive convex approximation (SCA) and dual decomposition, the problem of max-min fairness is solved. Through both theoretical analysis and simulations, the convergence and the effectiveness of the proposed algorithms are proved and evaluated. Simulation results show that the proposed algorithms can significantly improve the average EE of users.

This paper describes the tradeoff between fairness criteria of different allocation policies and throughput in a general network. A class of utility functions parameterized by a scalar defines a bandwidth allocation policy. An allocation is fair if the scalar is large and efficient if the aggregate source rate is large. The conjecture depends on the network topology in terms of routing and link capacities. A fairer allocation is always more efficient.

The problem of fair and feasible allocation of user throughputs in capacitated networks is investigated. The main contribution of the paper is an extension of network fairness, and in particular, max-min fairness from the traditional fixed- path model to a more versatile, routing-independent model. We show that the set of throughput configurations realizable in a capacitated network makes up a polyhedron, which gives rise to a max-min fair allocation completely analogous to the conventional case.

The issue of efficiency and fairness in resource allocation will continue to be of significant importance in scheduler designs for future wireless systems. Of particular importance is the development of distributed techniques for achieving desired efficiency and fairness tradeoffs. We will focus on the the design of selfishly efficient and as well as different fair policies through the use of a virtual timer. This virtual timer unifies various previously considered fairness methods including Round-Robin, Max-Min and Proportional fairness, which are rigorously investigated. The performance of the presented techniques are compared using extensive simulations.

Fairness comparison of FAST TCP and TCP Reno

In modern data intensive computing, it is increasingly common for jobs to be executed in a distributed fashion across multiple machine clusters or datacenters to take advantage of data locality. This paper studies fair resource allocation among jobs requiring distributed execution. We extend conventional max-min fairness for resource allocation in a single machine or machine cluster to distributed job execution over multiple sites and define Aggregate Max-min Fairness (AMF) which requires the aggregate resource allocation across all sites to be max-min fair. We show that AMF satisfies the properties of Pareto efficiency, envy-freeness and strategy-proofness, but it does not necessarily satisfy the sharing incentive property. We propose an enhanced version of AMF to guarantee the sharing incentive property. We present algorithms to compute AMF allocations and propose an add-on to optimize the job completion times under AMF. Experimental results show that compared with a baseline which simply requires the resource allocation at each site to be max-min fair, AMF performs significantly better in balancing resource allocation and in job completion time, particularly when the workload distribution of jobs among sites is highly skewed.

Fairness and system throughput, influenced by wireless interference, are major objectives of resource allocation in wireless networks. Whereas traditionally max-min fairness protocols have been developed for wired networks (where interference is not a factor for network performance), in this paper we investigate the problem of flow routing and fair bandwidth allocation under the constraints of the physical interference model for multihop wireless networks. A max-min fair (MMF) routing algorithm under physical interference constraints is proposed where multiple candidate paths coexist for each user to the base station. The algorithm is formulated as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors. We compare our approach with two MMF interference routing algorithms in the literature that use the protocol interference model to garner fair bandwidth allocation. We show that our algorithm performs better in terms of blocking probability, bandwidth usage and link load.

A start-up embedding in three business network settings – A matter of resource combining

It has been analytically proved that both MaxNet and SumNet can achieve max-min fair allocation in static networks. In SumNet networks, such as the TCP algorithm of the current Internet, the source rate is controlled by congestion signal which is the sum of signals from all of the congested links along the path from the source to the destination. In MaxNet networks, only the most congested link generates the control signal to dictate the source rate. This paper investigates the practical aspects of both schemes, specifically, the convergence speed and fairness tracking capability under transient network conditions. We have shown that the stability of SumNet's max-min fairness heavily depends on the network load. Within the stable ranges of operating points, SumNet's max-min fair allocation is obtained at the cost of response speed. An enhanced approach is proposed to improve the stability of SumNet to meet the max-min fairness criterion. We have shown both analytically and by simulation that under stable conditions, Maxnet has faster convergence speed and better fairness tracking capability than SumNet in a highly volatile environment.

Fairness in Routing and Load Balancing

In order to improve the total system throughput by analyzing the resource allocation problems in backhaul links and access links of base stations in ultra-dense network (UDN), a proportional fair resource allocation (PFRA) algorithm was designed for the wireless hybrid hierarchical backhaul network in this paper. First, the base stations were stratified and the frame structure of each base station in different layeies was re-designed. Then, the objective function was constructed based on the proportional fair utility function to optimize the system throughput. Finally, an iterative algorithm was derived based on Lagrange Multiplier algorithm to obtain the optimal solution of spectrum resource allocation in hybrid hierarchical backhaul network. The performance of the PFRA algorithm has been simulated and the results show that the PFRA algorithm can improve network throughput effectively at the cost of losing a certain network coverage rate. In the future, PFRA needs to be optimized to reach the best balance between network throughput and network coverage rate.

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.