分布式抢占决策：概率图模型、算法与近优性
Distributed Preemption Decisions: Probabilistic Graphical Model, Algorithm and Near-Optimality
摘要：
协同决策是未来网络管理和控制的一种设想。分布式连接抢占是一个重要的例子，其中节点可以做出智能决策，分配资源和控制业务流的多类服务网络。一个挑战是，节点的决定是空间依赖的流量侵入网络中的多个节点。因此，性能-复杂性权衡变得重要，即，决策的准确性与节点之间交换的信息量之间的关系。连接抢占是NP完全的。集中式抢占是最佳的，但在计算上难以处理。分散式抢占在计算上是高效的，但可能导致较差的性能。这项工作研究分布式抢占节点决定是否和哪些流抢占只使用本地信息交换与邻居。我们开发，基于概率图模型，一个接近最优的分布式算法。每个节点使用该算法来集体做出接近最优的抢占决策。我们研究了接近最优的性能和复杂性之间的权衡，对应于分布式算法的信息交换量。通过理论分析和仿真验证了该算法的有效性。
英文摘要：
Cooperative decision making is a vision of future network management and control. Distributed connection preemption is an important example where nodes can make intelligent decisions on allocating resources and controlling traffic flows for multi-class service networks. A challenge is that nodal decisions are spatially dependent as traffic flows trespass multiple nodes in a network. Hence the performance-complexity trade-off becomes important, i.e., how accurate decisions are versus how much information is exchanged among nodes. Connection preemption is known to be NP-complete. Centralized preemption is optimal but computationally intractable. Decentralized preemption is computationally efficient but may result in a poor performance. This work investigates distributed preemption where nodes decide whether and which flows to preempt using only local information exchange with neighbors. We develop, based on the probabilistic graphical models, a near-optimal distributed algorithm. The algorithm is used by each node to make collectively near-optimal preemption decisions. We study trade-offs between near-optimal performance and complexity that corresponds to the amount of information-exchange of the distributed algorithm. The algorithm is validated by both analysis and simulation.
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