You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Abstract In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.

Cited Abstract(s):
[1]: Current data network scenario makes traffic engineering (TE) a very challenging task. The ever growing access rates and new applications running on end-hosts result in more variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, load-balancing has proved itself an excellent tool to face this uncertainty. In particular, mechanisms where routers greedily minimize a path cost function (thus requiring minimum coordination) have been studied from a game-theoretic perspective in what is known as a routing game (RG). The contribution of this paper is twofold. We first propose a new RG specifically designed for elastic traffic, where we maximize the total utility through load-balancing only. Secondly, we consider several important RGs from a TE perspective and, using several real topologies and traffic demands, present a thorough comparison of their performance. This paper brings insight into several RGs, which will help one in choosing an adequate dynamic load-balancing mechanism. The comparison shows that the performance gain of the proposed game can be important.
Work [1] considers both congestion game and bottleneck game, in the application to the routing problem. It also proposes a new routing game specifically for the elastic flows. All three approaches are compared. Basing on one example and two real network experiments, some advantages of the introduced game are shown.