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Performance analysis of affinity clustering on transaction processing coupling architecture

机译:事务处理耦合架构上的亲和力聚类性能分析

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Coupling multiple computing nodes for transaction processing has become increasingly attractive for reasons of capacity, cost, and availability. This paper presents a comparison of robustness (in terms of performance) of three different architectures for transaction processing. In the shared nothing (SN) architecture, neither disks nor memories are shared. In the shared disk (SD) architecture, all disks are accessible from all nodes, whereas in the shared intermediate memory (SIM) architecture, a shared intermediate level of memory is introduced. Coupling multiple nodes inevitably introduces certain interferences and overheads, which take on different forms and magnitudes under the different architectures. Affinity clustering, which attempts to partition the transactions into affinity clusters according to their database reference patterns, can be employed to reduce the coupling degradation under the different architectures, though in different ways. However, the workload may not be partitionable into N affinity clusters of equal size, where N is the number of nodes in the coupled system, so that the load can be evenly spread over all nodes. In addition to balancing the load, we need to maintain a large fraction of data references within the database affiliated with the affinity cluster. These become increasingly harder to achieve for large values of N. In this paper, we examine the impact of affinity on the performance of these three different coupling architectures.
机译:由于容量,成本和可用性的原因,耦合多个计算节点进行事务处理变得越来越有吸引力。本文介绍了三种不同的事务处理体系结构的鲁棒性(就性能而言)。在“不共享”(SN)体系结构中,磁盘和内存均不共享。在共享磁盘(SD)架构中,可以从所有节点访问所有磁盘,而在共享中间存储器(SIM)架构中,引入了共享的中间级别的内存。耦合多个节点不可避免地会引入某些干扰和开销,这些干扰和开销在不同架构下会呈现不同的形式和大小。可以采用亲和力聚类尝试根据其数据库参考模式将事务划分为亲和力聚类,尽管这种方式不同,但可以用来减少不同体系结构下的耦合降级。但是,工作负载可能无法划分为相等大小的N个亲和力群集,其中N是耦合系统中的节点数,因此负载可以均匀地分布在所有节点上。除了平衡负载之外,我们还需要在与亲和性群集关联的数据库中维护很大一部分数据引用。对于较大的N值,实现这些目标变得越来越困难。在本文中,我们研究了亲和力对这三种不同耦合体系结构性能的影响。

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