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A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

机译:MapReduce中运行容量分配的博弈论方法

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摘要

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that service level agreements (SLAs) are met and avoiding wastes. In this paper we consider mathematical models for the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers.
机译:如今,许多公司都拥有大量原始的,非结构化的数据。在支持大数据的技术中,MapReduce框架(尤其是其开源实现Apache Hadoop)占据着中心位置。出于成本效益的考虑,一种通用方法需要在多个用户之间共享服务器群集。基础基础设施应为每个用户提供公平的计算资源份额,以确保满足服务水平协议(SLA)并避免浪费。在本文中,我们考虑了用于在Hadoop 2.x集群中优化计算资源分配的数学模型,旨在开发新的容量分配技术,以确保共享数据中心的更好性能。我们的目标是在不违反SLA规定的期限的前提下,大幅降低功耗,并避免因工作被拒而带来的罚款。该方法的核心是基于博弈论模型和技术的用于分配运行时容量的分布式算法,该算法通过交互播放器(即中央资源管理器和类管理器)来模拟MapReduce动态。

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