A distributed algorithm for equitable bandwidth allocation for content distribution in a tree network

Luss, Hanan

doi:10.1057/jors.2011.53

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…Another still underexplored area of fair network optimization is related to distributed optimization process and related models [174]. In some equitable optimization problems, as shown in [113], the optimization algorithm can be implemented in a distributed mode where most of the computations are done independently and in parallel at the nodes. However, in most cases the distributed approaches to fairness must be based on game theory rather than on direct optimization [175][176][177].…”

Section: Discussionmentioning

confidence: 99%

“…In [111] the MMF model is introduced and a lexicographic max-min algorithm is presented. As shown in [113] the algorithm can be implemented in a distributed mode where most of the computations are done independently and in parallel at all nodes, while some information is exchanged among the nodes. More complex content distribution models and corresponding algorithms are discussed in [114][115][116].…”

Section: Content Distribution Networkmentioning

confidence: 99%

See 1 more Smart Citation

Fair Optimization and Networks: A Survey

Ogryczak

Luss

Pióro

et al. 2014

Journal of Applied Mathematics

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Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness). The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.

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Section: Discussionmentioning

confidence: 99%

Section: Content Distribution Networkmentioning

confidence: 99%

Fair Optimization and Networks: A Survey

Ogryczak

Luss

Pióro

et al. 2014

Journal of Applied Mathematics

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“…Applications include bandwidth or channel allocation (Tomaszewski, 2005;Lee et al, 2004;Lee & Cho, 2007;Luss, 2008;Salles & Barria, 2008;Ogryczak et al, 2008;Luss, 2010;Luss, 2012a;Jeong et al, 2005;Chang et al, 2006;Zukerman et al, 2008;Morell et al, 2008;Zhang & Ansari, 2010;Bonald et al, 2006;Heikkinen, 2004;Ogryczak et al, 2005;Kunqi et al, 2007), water rights allocation (Udías et al, 2012), health care planning (Earnshaw et al, 2007;Demirci et al, 2012;Hooker & Williams, 2012;Bertsimas et al, 2013), WIP (Kanban) allocation in production systems (Ryan & Vorasayan, 2005), fixed cost allocation (Li et al, 2013;Butler & Williams, 2006), and public resource allocation such as allocating voting machines to election precincts . There are also studies that consider general resource allocation settings such as Bertsimas et al (2011), Hooker (2010, Nace and Orlin (2007), Medernach and Sanlaville (2012) and Bertsimas et al (2014).…”

Section: Allocation Problemsmentioning

confidence: 99%

“…Bertsimas et al (2014) propose a modeling framework for general dynamic resource allocation Table 2 Classical problems in OR re-considered with equity concerns. Tomaszewski (2005), Lee, Moon, and Cho (2004), Lee and Cho (2007), Luss (2008), Salles and Barria (2008), Ogryczak, Wierzbicki, and Milewski (2008), Luss (2010), Luss (2012a), Jeong, Kim, and Lee (2005), Chang, Lee, and Kim (2006), Zukerman, Mammadov, Tan, Ouveysi, and Andrew (2008), Morell, Seco-Granados, and Vázquez-Castro (2008), Zhang and Ansari (2010), Bonald, Massoulié, Proutière, andVirtamo (2006), Heikkinen (2004), Ogryczak, Pioro, and Tomaszewski (2005), Udías, Ríos Insua, Cano, and Fellag (2012), Earnshaw, Hicks, Richter, and Honeycutt (2007), Demirci, Schaefer, Romeijn, and Roberts (2012), Hooker and Williams (2012), Bertsimas, Farias, and Trichakis (2013), Ryan and Vorasayan (2005), Li, Yang, Chen, Dai, and Liang (2013), Butler and Williams (2006), Yang, Allen, Fry, and Kelton (2013), Trichakis (2011), Bertsimas, Farias, andTrichakis (2012), Hooker (2010), Nace and Orlin (2007), Medernach and Sanlaville (2012), Bertsimas, Gupta, and Lulli (2014), Karsu and Morton (2014), Johnson, Turcotte, and Sullivan (2010), Kozanidis (2009), Eiselt and Marianov (2008), Vossen and Ball (2006), …”

Section: Allocation Problemsmentioning

confidence: 99%

Inequity averse optimization in operational research

Karsu

Morton

2015

European Journal of Operational Research

150

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a b s t r a c tThere are many applications across a broad range of business problem domains in which equity is a concern and many well-known operational research (OR) problems such as knapsack, scheduling or assignment problems have been considered from an equity perspective. This shows that equity is both a technically interesting concept and a substantial practical concern. In this paper we review the operational research literature on inequity averse optimization. We focus on the cases where there is a tradeoff between efficiency and equity.We discuss two equity related concerns, namely equitability and balance. Equitability concerns are distinguished from balance concerns depending on whether an underlying anonymity assumption holds. From a modeling point of view, we classify three main approaches to handle equitability concerns: the first approach is based on a Rawlsian principle. The second approach uses an explicit inequality index in the mathematical model. The third approach uses equitable aggregation functions that can represent the DM's preferences, which take into account both efficiency and equity concerns. We also discuss the two main approaches to handle balance: the first approach is based on imbalance indicators, which measure deviation from a reference balanced solution. The second approach is based on scaling the distributions such that balance concerns turn into equitability concerns in the resulting distributions and then one of the approaches to handle equitability concerns can be applied.We briefly describe these approaches and provide a discussion of their advantages and disadvantages. We discuss future research directions focussing on decision support and robustness.

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“…Accordingly, a utility equilibrium is obtained when none of these users changes their connections or prices. Meanwhile, a bandwidth allocation algorithm [9] is designed to simulate the bandwidth allocation process, it can improve the utilities of all the desktop users during the process of pricing iterations and ensure the achievement of utility equilibrium for mobile users in the same group.…”

Section: Introductionmentioning

confidence: 99%