This paper presents a multi-player multi-issue negotiation model to solve a resource allocation problem. We design a multilateral negotiation protocol, by which rational players bid sequentially in consecutive rounds till a deadline. Every player's bid is a combination of all resource allocations for himself. In this framework, we perform a thorough theoretical analysis of the negotiation with complete information, which is a preliminary for the more complex incomplete information case. We show that, under a complete information setting, we can derive the negotiation strategies that form a subgame perfect equilibrium outcome. We also show that when a discount factor exists, an agreement will be reached immediately at the end of the first negotiation round. By making trade-offs between issues, the utility that every player gets in the equilibrium outcome is maximized and the solution is Pareto optimal.
Abstract-The design of efficient and scalable streaming Content Distribution Networks (CDNs) is an open problem of great economic interest. A key decision is the number of replica servers to use and their location to balance network bandwidth and server costs. This problem is highly complex, since it is interrelated to the streaming media delivery protocols, client redirecting decisions, and the content delivery routing problem.To address this design problem, we develop heuristics for the server placement, the server selection, and the routing problem. In contrast to previous work, we study both periodic broadcast and hierarchical merging protocols using multiple media files with different request rates. The heuristics lead to highly accurate and fast approximations. We also analyze the relationship between the start-up delay caused by some delivery protocols, the bandwidth consumption, and the number of replica servers, providing complete and fully implementable solutions to the design of streaming CDNs.
Abstract. In the global logistics market, agents need to decide upon whether to accept jobs offered sequentially. For each offer, an agent makes an immediate selection decision with little knowledge about future jobs; the goal is to maximize the profit. We study this online decision problem of acceptance of unit length jobs with time constraints, which involves online scheduling. We present theoretically optimal acceptance strategies for a fundamental case, and develop heuristic strategies in combination with an evolutionary algorithm for more general and complex cases. We show experimentally that in the fundamental case the performance of heuristic solutions is almost the same as that of theoretical solutions. In various settings, we compare the results achieved by our online solutions to those generated by the optimal offline solutions; the average-case performance ratios are about 1.1. We also analyze the impact of the ratio between the number of slots and the number of jobs on the difficulty of decisions and the performance of our solutions.
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