Utility Max-min Fairness Research Articles

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.

Utility Max-Min Fair Link Adaptation in IEEE 802.11ac Downlink Multi-User

In this letter, we propose a novel model and corresponding algorithms to address the optimal utility max-min fair link adaptation in Downlink Multi-User (DL-MU) feature of the emerging IEEE 802.11ac WLAN standard. Herein, we first propose a simple yet accurate model to formulate the max-min fair link adaptation problem. Furthermore, this model guarantees the minimum utility gain of each receiver according to its requirements. In the second step, we show that the optimal solution of the proposed model can be obtained in polynomial time, and then the solution algorithms are proposed and analyzed. The simulation results demonstrate the significant achievement of the proposed utility-aware link adaptation approach in terms of max-min fairness and utility gain compared to utility-oblivious schemes.

Utility max–min fair resource allocation for communication networks with multipath routing

This paper considers the flow control and resource allocation problem as applied to the generic multipath communication networks with heterogeneous applications. We propose a novel distributed algorithm, show and prove that among all the sources with positive increasing and bounded utilities (no need to be concave) in steady state, the utility max–min fairness is achieved, which is essential for balancing Quality of Service (QoS) for different applications. By combining the first order Lagrangian method and filtering mechanism, the adopted approach eliminates typical oscillation behavior in multipath networks and possesses a rapid convergence property. In addition, the algorithm is capable of deciding the optimal routing strategy and distributing the total traffic evenly out of the available paths. The performance of our utility max–min fair flow control algorithm is evaluated through simulations under two representative case studies, as well as the real implementation issues are addressed deliberately for the practical purpose.

A simple framework of utility max-min flow control using sliding mode approach

Motivated by the limitations of current optimal flow control approach, we develop a new utility max-min flow control framework using classic sliding mode control. It consists of a source algorithm and a binary congestion feedback mechanism, in which only the sources with the highest utility at each congested link are required to reduce their transmission rates. It can be directly applied to a multi-service network with heterogeneous applications that have different QoS characteristics. The proposed framework achieves the utility max-min fairness among applications efficiently in the sense of low overhead and rapid convergence. Rigorously, the system is proven to be asymptotically stable by means of Lyapunov's theorem.

Congestion control in utility fair networks

This paper deals with a congestion control framework for elastic and real-time traffic, where the user's application is associated with a utility function. We allow users to have concave as well as non-concave utility functions, and aim at allocating bandwidth such that utility values are shared fairly. To achieve this, we transform all utilities into strictly concave second order utilities and interpret the resource allocation problem as the global optimization problem of maximizing aggregate second order utility. We propose a new fairness criterion, utility proportional fairness, which is characterized by the unique solution to this problem. Our fairness criterion incorporates utility max-min fairness as a limiting case. Based on our analysis, we obtain congestion control laws at links and sources that (i) are linearly stable regardless of the network topology, provided that a bound on round-trip-times is known, (ii) provide a utility proportional fair resource allocation in equilibrium. We further investigate the efficiency of utility fair resource allocations. Our measure of efficiency is defined as the worst case ratio of the total utility of a utility proportional fair rate vector and the maximum possible total utility. We present a generic technique, which allows to obtain upper bounds on the efficiency loss. For special cases, such as linear and concave utility functions, and non-concave utility functions with bounded domain, we explicitly calculate such upper bounds. Then, we study utility fair resource allocations with respect to bandwidth fairness. We derive a fairness metric assessing the aggressiveness of utility functions. This allows us to design fair utility functions for various applications. Finally, we simulate the proposed algorithms using the NS2 simulator.

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

On achieving fairness in the joint allocation of processing and bandwidth resources: principles and algorithms

The problem of achieving fairness in the allocation of the bandwidth resource on a link shared by multiple flows of traffic has been extensively researched over the last decade. However, with the increasing pervasiveness of optical networking and the occasional trend toward using over-provisioning as the solution to bandwidth congestion, a router's processor also becomes a critical resource to which, ideally speaking, all competing flows should have fair access. For example, achieving fairness in the allocation of processing resources can be part of an overall strategy of countering certain kinds of denial of service attacks (such as those based on an excessive use of the router processor by using unnecessary optional headers). In this paper, we investigate the issue of achieving fairness in the joint allocation of the processing and bandwidth resources. We first present a simple but powerful general principle for defining fairness in such systems based on any of the classic notions of fairness such as max-min fairness, proportional fairness, and utility max-min fairness defined for a single resource. We apply our principle to a system with a shared processor and a shared link with max-min fairness as the desired goal. We then propose a practical and provably fair packet-by-packet algorithm for the joint allocation of processing and bandwidth resources. We demonstrate the fairness achieved by our algorithm through simulation results using both synthetic and real gateway traffic traces. The principles and the algorithm detailed in this paper may also be applied in the allocation of other kinds of resources such as power, which is a critical resource in mobile systems.

Toward end-to-end fairness: a framework for the allocation of multiple prioritized resources in switches and routers

As flows of traffic traverse a network, they share with other flows a variety of resources such as links, buffers and router CPUs in their path. Fairness is an intuitively desirable property in the allocation of resources in a network shared among flows of traffic from different users. While fairness in bandwidth allocation over a shared link has been extensively studied, overall end-to-end fairness in the use of all the resources in the network is ultimately the desired goal. End-to-end fairness becomes especially critical when fair allocation algorithms are used as a component of the mechanisms used to provide end-to-end quality-of-service guarantees. This paper seeks to answer the question of what is fair when a set of traffic flows share multiple resources in the network with a shared order of preference for the opportunity to use these resources. We present the Principle of Fair Prioritized Resource Allocation or the FPRA principle, a powerful extension of any of the classic notions of fairness such as max–min fairness, proportional fairness and utility max–min fairness defined over a single resource. We illustrate this principle by applying it to a system model with a buffer and an output link shared among competing flows of traffic. To complete our illustration of the applicability of the FPRA principle, we propose a measure of fairness and evaluate representative buffer allocation algorithms based on this measure. Besides buffer allocation, the FPRA principle may also be used in other contexts in data communication networks and operating system design.