面向多媒体流的考虑用户满意度与前景理论效用的资源分配
User Satisfaction-Aware Resource Allocation with Prospect-Theoretic Utility for Multimedia Streaming
AI总结：
针对多媒体流资源分配未考虑用户满意度的非对称效用问题，基于前景理论提出通用效用度量与资源分配策略，通过多队列系统理论分析得出快速最优阈值确定方法，其性能优于现有公平分配策略。
AI中文摘要：
传统多媒体流资源分配策略主要针对吞吐量、公平性和延迟等指标。然而，用户满意度受吞吐量波动的强烈影响。尽管近期一些研究已考虑吞吐量波动，但它们忽略了过去几十年重塑行为经济学的一个著名人类行为方面：相同幅度的吞吐量下降带来的困扰比同等幅度吞吐量提升带来的满意度更大。受经济学领域这一被称为前景理论的见解启发，我们提出了一种通用效用（度量），可同时捕获平均吞吐量和吞吐量波动对用户满意度的非对称影响。接着，我们提出一种资源分配策略，该策略在系统从资源充足状态过渡到资源稀缺状态时，会减少分配资源的突然下降，同时尊重不同用户类别的优先级。大量仿真表明，该策略的性能优于现有的公平分配策略。不过，最优阈值的选择取决于网络实例，而在实际应用中网络实例可能频繁变化。因此，基于多队列系统的理论分析，我们提出了可用于现场部署的快速数值方法来确定最优阈值。
英文摘要：
Traditional resource allocation policies for multimedia streaming have primarily targeted metrics such as throughput, fairness and delay. However, user satisfaction is known to be strongly influenced by variations in throughput. Although some recent works have considered throughput variations, they overlooked a well-known aspect of human behavior that has reshaped behavioral economics over the past few decades: the same amount of decrease in throughput causes greater annoyance than the satisfaction caused by the increase. Motivated by this insight from the broad field of economics, popularly known as prospect theory, we propose a generic utility (metric) that captures both the average throughput and the asymmetric effect of throughput variations on user satisfaction. Next, we propose a resource allocation policy that reduces abrupt decreases in allocated resources when the system transitions from a resource-rich to resource-scarce state while respecting the priorities of different classes of users. Extensive simulations show that this policy outperforms the existing fair allocation policies. However, the choice of the optimal threshold depends on the network instance, which, in practice, may change frequently. Hence, building on the theoretical analysis of multi-queue systems, we propose fast numerical methods for determining the optimal threshold, which can be used in field deployments.
