Discrete bandwidth allocation considering fairness and transmission load in multicast networks

Abstract

As a promising solution to tackle the network heterogeneity in multicasting, layered multicast protocols such as Receiver-driven layered multicast (RLM) and Layered video multicast with retransmission (LVMR) have been proposed. This paper considers fairness as well as transmission load in the layered multicasting. Lexicographically fair bandwidth allocation among multicast receivers is considered under the constraint of minimum bandwidth requirement and the link capacity of the network. The problem of transmission load in the layer multicasting due to various user requirements is also examined by minimizing the number of layers.The bandwidth allocation is formulated as a nonlinear integer programming problem. A dual objective tabu search is proposed to solve the fairness and transmission load problem in multicast networks. Outstanding performance is obtained by the proposed tabu search. When the fairness objective is considered, the solution gap from the optimal solution is less than 2% in problems with 50 virtual sessions. The complexity of the dual objective largely depends on the weighting factor of the two objectives. Even in tough cases the proposed tabu search provides excellent solution the gap of which is within 6% from the optimal solution.

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

“…This is an NP-complete problem and they provide an algorithm for a simpler objective function than the lexicographic maximin objective. Lee et al (2004) and Lee and Cho (2007) also present algorithms for the discrete allocation case, using an approximated objective function solved by meta-heuristics. Luss (2008) presents an algorithm for continuous bandwidth allocations using a different formulation.…”

Equitable bandwidth allocation in content distribution networks

“…Sarkar and Tassiulas [42] define a new notion of fairness, referred to as a maximally fair objective which is weaker than the lexicographic maximin objective, and provide a polynomial algorithm for that objective. Lee and Cho [22] and Lee, Moon, and Cho [23] formulate the problem as a convex optimization problem. They minimize the sum of convex decreasing functions as an approximation to a lexicographic maximin objective, using tabu search and genetic algorithm heuristics.…”

Inequity averse optimization in operational research

“…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), …”

“…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).…”