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Competitive Clustering of Stochastic Communication Patterns on a Ring

机译:戒指上随机通信模式的竞争聚类

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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 l 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 benefitting 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(logn)-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个节点之间的成对通信请求的在线序列(例如,任务或虚拟机)。我们的目标是通过将通信节点划分为大小k的L簇(例如,物理服务器)来最小化通信成本(例如,虚拟机槽数)。我们假设如果通信节点位于同一群集中,则通信请求成本为0;如果节点位于不同的群集中,请求以成本1,远程使用群集间通信远程使用群集通信。此外,我们可以迁移:从一个群集到另一个簇的节点以成本α≥1。我们启动了对随机问题变体的研究,其中通信模式遵循固定的分布,由对手设置。因此,在线算法需要在迅速移动到看似良好的配置(低间集间通信成本)之间找到良好的权衡,以及过早地结束配置的风险,后来变得坏,所以高迁移成本。我们的主要技术贡献是一个确定性在线算法,其是o(logn) - 具有高概率(w.h.p.)的竞争力,用于特定但基本类别的问题:即环形图。

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