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Multiresource allocation: Fairnessefficiency tradeoffs in a unifying framework
 in Proc. IEEE INFOCOM
, 2012
"... Abstract—Quantifying the notion of fairness is underexplored when there are multiple types of resources and users request different ratios of the different resources. A typical example is datacenters processing jobs with heterogeneous resource requirements on CPU, memory, network, bandwidth, etc. T ..."
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Abstract—Quantifying the notion of fairness is underexplored when there are multiple types of resources and users request different ratios of the different resources. A typical example is datacenters processing jobs with heterogeneous resource requirements on CPU, memory, network, bandwidth, etc. This paper develops a unifying framework addressing the fairnessefficiency tradeoff in light of multiple types of resources. We develop two families of fairness functions that provide different tradeoffs, characterize the effect of user requests ’ heterogeneity, and prove conditions under which these fairness measures satisfy the Pareto efficiency, sharing incentive, and envyfree properties. Intuitions behind the analysis are explained in two visualizations of multiresource allocation. We also investigate people’s fairness perceptions through an online survey of allocation preferences and provide a brief overview of related work on fairness.
1 Optimal Tradeoff between SumRate Efficiency and Jain’s Fairness Index in Resource Allocation
"... Abstract—The focus of this paper is on studying the tradeoff between the sum efficiency and Jain’s fairness index in general resource allocation problems. Such problems are frequently encountered in wireless communication systems with M users. Among the commonlyused methods to approach these proble ..."
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Abstract—The focus of this paper is on studying the tradeoff between the sum efficiency and Jain’s fairness index in general resource allocation problems. Such problems are frequently encountered in wireless communication systems with M users. Among the commonlyused methods to approach these problems is the one based on the αfair policy. Analyzing this policy, it is shown that, except for the case of M = 2 users, this policy does not necessarily achieve the optimal EfficiencyJain tradeoff (EJT). In particular, it is shown that, when the number of users M> 2, the gap between the efficiency achieved by the αfair policy and that achieved by the optimal EJT policy can be unbounded, for the same Jain’s index. Finding the optimal EJT corresponds to solving potentially difficult nonconvex optimization problems. To alleviate this difficulty, we derive sufficient conditions, which are shown to be sharp and naturally satisfied in various radio resource allocation problems. These conditions provide us with a means for identifying cases in which finding the optimal EJT and the rate vectors that achieve it can be reformulated as convex optimization problems. The new formulations are used to devise computationallyefficient resource schedulers that enable the optimal EJT to be achieved for both quasistatic and ergodic timevarying communication scenarios. Analytical findings are confirmed by numerical examples. I.
Fairness comparison of FAST TCP and TCP Vegas
"... ABSTRACT: This paper compares the equilibrium properties of FAST TCP and TCP Vegas. Although the two have the same equilibrium point when all sources know their true propagation delays, FAST is fairer when there are estimation errors. The performance of Vegas approaches that of FAST when the queuein ..."
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ABSTRACT: This paper compares the equilibrium properties of FAST TCP and TCP Vegas. Although the two have the same equilibrium point when all sources know their true propagation delays, FAST is fairer when there are estimation errors. The performance of Vegas approaches that of FAST when the queueing delay is very much less than the propagation delay. 1.
Towards Efficient and Fair Radio Resource Allocation Schemes for InterferenceLimited Cellular Networks
, 2013
"... ii The focus of this thesis is on studying the tradeoff between efficiency and fairness in interferencelimited cellular networks. We start by characterizing the optimal tradeoff between efficiency and fairness in general resource allocation problems, including those encountered in cellular network ..."
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ii The focus of this thesis is on studying the tradeoff between efficiency and fairness in interferencelimited cellular networks. We start by characterizing the optimal tradeoff between efficiency and fairness in general resource allocation problems, including those encountered in cellular networks, where efficiency is measured by the sumrate and fairness is measured by the Jain’s fairness index. Among the commonlyused methods to approach these problems is the one based on the αfair policy. Analyzing this policy, we show that it does not necessarily achieve the optimal EfficiencyJain Tradeoff (EJT) except for the case of two users. When the number of users is greater than two, we prove that the gap between the efficiency achieved by the αfair policy and that achieved by the optimal EJT policy for the same Jain’s index can be unbounded. Finding the optimal EJT corresponds to solving potentially difficult nonconvex optimization problems. To alleviate this difficulty, we derive sufficient conditions, which are shown to be sharp and naturally satisfied in various radio re
To be Fair or Efficient or a Bit of Both ⋆
"... Introducing a new concept of (α, β)fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0 ≤ α ≤ 1, nor more than β ≥ 1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subje ..."
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Introducing a new concept of (α, β)fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0 ≤ α ≤ 1, nor more than β ≥ 1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunications network for an arbitrary fairness criterion and cost functions. We formulate a nonlinear program (NLP) that finds the optimal bandwidth allocation by maximizing efficiency subject to (α, β)fairness constraints. This leads to what we call an efficiencyfairness function, which shows the benefit in efficiency as a function of the extent to which fairness is compromised. To solve the NLP we use two algorithms. The first is a well known branchandboundbased algorithm called Lipschitz Global Optimization and the second is a recently developed algorithm called Algorithm for Global Optimization Problems (AGOP). We demonstrate the applicability of the framework to a range of example from sharing a single link to efficiency fairness issues associated with serving customers in remote communities. Key words: nonlinear programming, utility optimization, fairness, efficiencyfairness tradeoff, bandwidth allocation.