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Competitive Clustering of Stochastic Communication Patterns on a Ring

机译:环上随机通信模式的竞争性聚类

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This paper studies a fundamental dynamic clustering problem. The input is an online sequence of pairwise communication requests between n nodes (e.g., tasks or virtual machines). Our goal is to minimize the communication cost by partitioning the communicating nodes into £ clusters (e.g., physical servers) of size k (e.g., number of virtual machine slots). We assume that if the communicating nodes are located in the same cluster, the communication request costs 0; if the nodes are located in different clusters, the request is served remotely using inter-cluster communication, at cost 1. Additionally, we can migrate: a node from one cluster to another at cost α ≥ 1. We initiate the study of a stochastic problem variant where the communication pattern follows a fixed distribution, set by an adversary. Thus, the online algorithm needs to find a good tradeoff between ben-efitting from quickly moving to a seemingly good configuration (of low inter-cluster communication costs), and the risk of prematurely ending up in a configuration which later turns out to be bad, entailing high migration costs. Our main technical contribution is a deterministic online algorithm which is O(log n)-competitive with high probability (w.h.p.), for a specific but fundamental class of problems: namely on ring graphs.
机译:本文研究了一个基本的动态聚类问题。输入是n个节点(例如任务或虚拟机)之间成对通信请求的在线序列。我们的目标是通过将通信节点划分为大小为k(例如,虚拟机插槽的数量)的群集(例如,物理服务器)来最大程度地降低通信成本。我们假设如果通信节点位于同一群集中,则通信请求的成本为0;否则,通信请求的成本为0。如果节点位于不同的群集中,则使用群集间通信以成本1远程处理请求。此外,我们可以将一个节点从一个群集迁移到另一个群集,成本为α≥1。我们开始进行随机研究问题变体,其中交流模式遵循由对手设置的固定分布。因此,在线算法需要在从快速过渡到看似好的配置(集群间通信成本低)的行之有效与过早地陷入后来被证明是糟糕的配置的风险之间找到良好的权衡,导致迁移成本高昂。我们的主要技术贡献是确定性在线算法,对于特定但基本的问题类别(即在圆环图上),它具有O(log n)-竞争性,并且具有很高的概率(w.h.p.)。

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