Max-min Fair Bandwidth Allocation Research Articles

Traffic-based adaptive bandwidth adjustment for flexible OTN connectivity in optical networks.

In conventional optical transport networks, the service form is the fixed bandwidth connectivity, which is not flexible for carrying bursting traffic. To support the time-varying traffic in an efficient way, researchers are studying the optical service units for building the more flexible optical transport network (OTN) connectivity, which is capable of dynamic hitless bandwidth adjustment. To better utilize the benefits of flexible connectivity, network operators need efficient algorithms to adjust the flexible connectivity bandwidth, especially in the network with a massive number of connections. In this paper, based on max-min fair bandwidth allocation criteria, we propose two traffic-based adaptive bandwidth adjustment algorithms to make bandwidth adjustment decisions, with the aim to improve bandwidth effectiveness. Simulation results show that the proposed algorithms can improve bandwidth utilization by up to 16%. Additionally, under high load conditions, it can reduce the loss in traffic bitrate by a maximum of 10%.

Distributed Traffic Engineering for Multi-Domain SDN Without Trust

In software defined networking, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flat</i> design of distributed control plane enables the management of multi-domain networks that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between independent local controllers due to the lack of centralized arbitration. Moreover, domains without trust may not be always cooperative and could even cheat to maximize their own interests. In this article, we first consider the cooperative scenario and address the problem of traffic engineering in a flat distributed control plane. We propose a fully distributed algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DisTE</monospace> , which can provide max-min fair bandwidth allocation for flows and maximize resource utilization. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DisTE</monospace> also preserves the local topology of each domain and achieves policy consistency by multiple rounds of synchronization. We then consider the non-cooperative scenario, where selfish domains may discriminate bandwidth requests from other domains or overstate theirs owns to squeeze more bandwidths.

Adaptive bandwidth binning for bandwidth management

In this paper, we present a novel bandwidth management scheme that we call adaptive bandwidth binning (ABB). ABB is presented in the context of a DOCSIS cable network, but it has obvious applicability to downstream service on any shared medium access network in which all downstream traffic is scheduled by a single headend device such as a CMTS or wireless base station or access point.ABB is capable of providing approximate weighted max-min fair sharing of downstream bandwidth via a low-overhead scheduler that requires only a small number of permanently allocated queues. A modern delay-based active queue management (AQM) technique is employed to control delays.The performance of ABB is evaluated via ns-2 simulations in which workloads include FTP, HTTP-based adaptive streaming (HAS), and web traffic targeted for different tiered service quality levels. Our results show that ABB is able to provide approximate weighted max-min fair bandwidth allocation among responsive high bandwidth flows while isolating them from low bandwidth and latency sensitive flows. The use of CoDel in each ABB queue is effective in managing latency as required. The use of flow weights in ABB supports service tiering in which subscribers pay more for higher service rates.

Balancing flow table occupancy and link utilization in software-defined networks

Software-Defined Networking (SDN) employs a centralized control with a global network view and provides great opportunities to improve network performance. However, due to the limitation of flow-table space at the switches and unbalanced traffic allocation on links, an SDN may suffer from flow-table overflow and inefficient bandwidth allocation among flows, increasing the controller’s burden and degrading network performance. In this paper, we present a dynamic routing scheme named DIFF that differentiates flows based on their impact on network resource and adaptively selects routing paths for them to mitigate the problems of flow-table overflow and inefficient bandwidth allocation. DIFF pre-generates a set of paths for each pair of source–destination edge switches and intelligently selects the paths from the pre-generated path-sets for new flows with an objective to balance flow-table utilizations. It adaptively reroutes some elephant flows to achieve maximum throughput under the rule of max–min fair bandwidth allocation. Simulation results show that DIFF simultaneously balances the flow-table and link utilizations, reduces the controller’s workload and packet delay, while increasing network throughput, compared with baseline schemes.

Modeling max–min fair bandwidth allocation in BitTorrent communities

This paper gives an exact mathematical programming model and algorithm of the max---min fairness bandwidth allocation problem in multi-swarm peer-to-peer content sharing community. The proposed iterative method involves solution of LP and MILP problems of large scale. Based on real-world data traces, numerical experiments demonstrate that the new algorithm is computationally faster than an earlier developed one for larger problem sizes, and it provides better numerical stability. Moreover, even if its execution is stopped after some initial steps it still grants feasible solution with good approximation to max---min fairness.

UFalloc: Towards Utility Max-min Fairness of Bandwidth Allocation for Applications in Datacenter Networks

Providing fair bandwidth allocation for applications is becoming increasingly compelling in cloud datacenters, as different applications compete for the shared datacenter network resources. While existing solutions mainly provide bandwidth guarantees for virtual machines (VMs) or tenants with the aim of achieving the VM-level or tenant-level fairness of bandwidth allocation, scant attention has been paid to providing bandwidth guarantees for applications to achieve the fairness of application performance (utility). In this paper, we introduce a rigorous definition of application-level utility max-min fairness, which guides us to develop a non-linear model to investigate the relationship between the utility fairness and bandwidth allocation for applications. Based on such a model, we further arbitrate the intrinsic tradeoff between the network bandwidth utilization and utility fairness of application bandwidth allocation, using a tunable fairness relaxation factor. To improve the bandwidth utilization while maintaining the strict utility fairness of bandwidth allocation, we design UFalloc, an application-level Utility max-min Fair bandwidth allocation strategy in datacenter networks. With extensive experiments using OpenFlow in Mininet virtual network environment, we demonstrate that UFalloc can achieve high utilization of network bandwidth while maintaining the utility max-min fair share of bandwidth allocation with a certain degree of fairness relaxation, yet with an acceptable computational overhead.

Discharging the Network From Its Flow Control Headaches: Packet Drops and HOL Blocking

Congestion control becomes indispensable in highly utilized consolidated networks running demanding applications. In this paper, proactive congestion management schemes for Clos networks are described and evaluated. The key idea is to move the congestion avoidance burden from the data fabric to a scheduling network, which isolates flows using per-flow request counters. The scheduling network comprises per-output arbiters that grant data packets after reserving space for them in the buffer memories in front of fabric outputs. Computer simulations show that this strategy eliminates head-of-line (HOL) blocking and its adversarial effects throughout the fabric, without having to drop packets. In particular, a simplified model describes this result as a synergy between proactive buffer reservations and fine-grained multipath routing. Two alternative designs are presented. The first one places all arbiters in a central control unit, is simpler, and has superior performance. The second is more scalable by distributing the arbiters over the switching elements of the Clos network and by routing the control messages to and from endpoint adapters via multiple paths. Computer simulations of the complete system demonstrate high throughput and low latency under any number of congested outputs. Weighted max-min fair allocation of fabric-output link bandwidth is also demonstrated. Furthermore, delay breakdowns show that the time that packets wait in fabric and resequencing buffers is minimized as a result of the reduced (and equalized across all fabric paths) in-fabric contention. Finally, the high throughput capability of the system is corroborated by a Markov chain analysis of output buffer credits.

Multipath Routing and Max-Min Fair QoS Provisioning under Interference Constraints in Wireless Multihop Networks

In this paper, we investigate the problem of flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. The isotonicity of the routing metric is proved using virtual network decomposition. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where multiple paths (determined by using the routing metric) coexist for each user to the base station. In order to solve the algorithm, we develop an optimization formulation that is modeled as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare our RI3M routing metric and LMX:M3F bandwidth allocation algorithm with various interference-based routing metrics and interference-aware bandwidth allocation algorithms established in the literature. We show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio, and bandwidth usage.

Improving XCP to achieve max–min fair bandwidth allocation

TCP is prone to be inefficient and unstable in high-speed and long-latency networks [1]. The eXplicit Control Protocol (XCP) is a new and promising protocol that outperforms TCP in terms of efficiency, stability, queue size, and convergence speed. However, Low et al. recently discovered a weakness of XCP. In a multi-bottleneck environment, XCP may achieve as low as 80% utilization at a bottleneck link and consequently some flows may only receive a small fraction of their max-min fair rates. This paper proposes iXCP, an improved version of XCP. Extensive simulations show that iXCP overcomes the weakness of XCP, and achieves efficient and fair bandwidth utilization in both single- and multi-bottleneck environments. In addition, we prove that iXCP is max-min fair in steady state. This result implies that iXCP is able to fully utilize bottleneck bandwidth. Simulations show that iXCP preserves the good properties of XCP, including negligible queue lengths, near-zero packet loss rates, scalability, and fast convergence. Simulations also show that iXCP overcomes the under-utilization and instability problem of P-XCP [2], and outperforms JetMax [3] in terms of link utilization in the presence of highly dynamic traffic.

Fair Bandwidth Allocation in Optical Burst Switching Networks

Optical burst switching (OBS) is a promising switching technology for next-generation Internet backbone networks. One of the design challenges is how to provide fair bandwidth allocation in OBS networks; the schemes proposed for general store-and-forward IP switching networks can not be used because of the non-buffering and un-fully utilized bandwidth characteristics of OBS networks. We propose a rate fairness preemption (RFP) scheme to achieve approximately weighted max-min fair bandwidth allocation in OBS networks. We present an analysis of the burst loss probability in RFP-based OBS networks. The analysis and simulation results show that the RFP scheme provides fair bandwidth allocation in OBS networks.

Centralized and Distributed Algorithms for Routing and Weighted Max-Min Fair Bandwidth Allocation

Given a set of demands between pairs of nodes, we examine the traffic engineering problem of flow routing and fair bandwidth allocation where flows can be split to multiple paths (e.g., MPLS tunnels). This paper presents an algorithm for finding an optimal and global per-commodity max-min fair rate vector in a polynomial number of steps. In addition, we present a fast and novel distributed algorithm where each source router can find the routing and the fair rate allocation for its commodities while keeping the locally optimal max-min fair allocation criteria. The distributed algorithm is a fully polynomial epsilon-approximation (FPTAS) algorithm and is based on a primal-dual alternation technique. We implemented these algorithms to demonstrate its correctness, efficiency, and accuracy.

Max-Min Fair Scheduling in Input-Queued Switches

Fairness in traffic management can improve the isolation between traffic streams, offer a more predictable performance, eliminate transient bottlenecks, mitigate the effect of certain kinds of denial-of-service attacks, and serve as a critical component of a quality-of-service strategy to achieve certain guaranteed services such as delay bounds and minimum bandwidths. In this paper, we choose a popular notion of fairness called max-min fairness and provide a rigorous definition in the context of input-queued switches. We show that being fair at the output ports alone or at the input ports alone or even at both input and output ports does not actually achieve an overall max-min fair allocation of bandwidth in a switch. Instead, we propose a new algorithm that can be readily implemented in a distributed fashion at the input and output ports to determine the exact max-min fair rate allocations for the flows through the switch. In addition to proving the correctness of the algorithm, we propose a practical scheduling strategy based on our algorithm. We present simulation results, using both real traffic traces and synthetic traffic, to evaluate the fairness of a variety of popular scheduling algorithms for input-queued switches. The results show that our scheduling strategy achieves better fairness than other known algorithms for input-queued switches and, in addition, achieves throughput performance very close to that of the best schedulers.

Fairness and Load Balancing in Wireless LANs Using Association Control

The traffic load of wireless LANs is often unevenly distributed among the access points (APs), which results in unfair bandwidth allocation among users. We argue that the load imbalance and consequent unfair bandwidth allocation can be greatly reduced by intelligent association control. In this paper, we present an efficient solution to determine the user-AP associations for max-min fair bandwidth allocation. We show the strong correlation between fairness and load balancing, which enables us to use load balancing techniques for obtaining optimal max-min fair bandwidth allocation. As this problem is NP-hard, we devise algorithms that achieve constant-factor approximation. In our algorithms, we first compute a fractional association solution, in which users can be associated with multiple APs simultaneously. This solution guarantees the fairest bandwidth allocation in terms of max-min fairness. Then, by utilizing a rounding method, we obtain the integral solution from the fractional solution. We also consider time fairness and present a polynomial-time algorithm for optimal integral solution. We further extend our schemes for the on-line case where users may join and leave dynamically. Our simulations demonstrate that the proposed algorithms achieve close to optimal load balancing (i.e., max-min fairness) and they outperform commonly used heuristics.

Utility Max-Min Flow Control Using Slope-Restricted Utility Functions

We present a network architecture for the distributed utility max-min flow control of elastic and nonelastic flows where utility values of users (rather than data rates of users) are enforced to achieve max-min fairness. The proposed link algorithm converges to utility max-min fair bandwidth allocation in the presence of round-trip delays without using the information of users' utility functions. To show that the proposed algorithm can be stabilized not locally but globally, we found that the use of nonlinear control theory is inevitable. Even though we use a distributed flow-control algorithm, it is shown that any kind of utility function can be used as long as the minimum slopes of the functions are greater than a certain positive value. Though our analysis is limited to the single-bottleneck and homogeneous-delay case, we believe that the proposed algorithm is the first to achieve utility max-min fairness with guaranteed stability in a distributed manner

Max-Min Fair Rate Allocation Scheme in Multirate Multicast Networks Using Supervisory Control

This paper describes a max-min fair rate allocation scheme in multicast networks based on the supervisory control framework for discrete event systems. Based on the discrete event model of multicast networks, we design a supervisor to guarantee the max-min fair allocation of bandwidth. Analysis and simulations are used to show that the controlled networks guarantee the max-min fair sharing with a low message exchange overhead and fast convergence time.

Fair distributed congestion control in multirate multicast networks

We study fairness of resource allocation in multirate, multicast networks. In multirate networks, different receivers of the same multicast session can receive service at different rates. We develop a mathematical framework to model the maxmin fair allocation of bandwidth with minimum and maximum rate constraints. We present a necessary and sufficient condition for a rate allocation to be maxmin fair in a multirate, multicast network. We propose a distributed algorithm for computing the maxmin fair rates allocated to various source-destination pairs. This algorithm has a low message exchange overhead, and is guaranteed to converge to the maxmin fair rates in finite time.

Maxmin fair scheduling in wireless ad hoc networks

We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.

Maxmin fair scheduling in wireless ad hoc networks

We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.