网内流处理的资源分配策略
Resource Allocation Strategies for In-Network Stream Processing
摘要：
在本文中，我们考虑的运营商映射问题，在网络流处理应用。网络内流处理包括将稳态操作符树应用于在网络上的各个位置处不断更新的多个数据对象。网络内流处理的示例包括传感器网络中的数据处理，或分布式关系数据库上的连续查询。我们在一个"构造性"场景中研究算子映射问题，即，在这种情况下，构建专用于购买具有各种成本和能力的处理服务器的应用的平台。目标是最小化平台的成本，同时确保应用程序实现最小的稳态吞吐量。本文的第一个贡献是一组相关的运营商安置问题的线性规划的形式化，并证明，即使是简单的版本的问题是NP-完全的。我们的第二个贡献是设计了几个多项式时间算法，通过大量的模拟和理论界的最优解进行评估。
英文摘要：
In this paper we consider the operator mapping problem for in-network stream processing applications. In-network stream processing consists in applying a tree of operators in steady-state to multiple data objects that are continually updated at various locations on a network. Examples of in-network stream processing include the processing of data in a sensor network, or of continuous queries on distributed relational databases. We study the operator mapping problem in a ``constructive'' scenario, i.e., a scenario in which one builds a platform dedicated to the application buy purchasing processing servers with various costs and capabilities. The objective is to minimize the cost of the platform while ensuring that the application achieves a minimum steady-state throughput. The first contribution of this paper is the formalization of a set of relevant operator-placement problems as linear programs, and a proof that even simple versions of the problem are NP-complete. Our second contribution is the design of several polynomial time heuristics, which are evaluated via extensive simulations and compared to theoretical bounds for optimal solutions.
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