GraphTM: An Efficient Framework for Supporting Transactional Memory in a Distributed Environment
GraphTM: An Efficient Framework for Supporting Transactional Memory in a Distributed Environment
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GraphTM：分布式环境中支持事务内存的高效框架
DOI：
10.1145/3369740.3369774
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sharma, Gokarna
中科院分区：
文献类型：
--
作者：
Poudel, Pavan;Sharma, Gokarna
在本文中，我们提出了GraphTM，一个有效的和可扩展的框架，在分布式环境中处理事务。分布式环境被建模为图，其中图的每个节点是发出事务的处理节点。事务用来执行的对象也在图节点上（初始位置可以是任意的）。事务在节点上执行，这些节点在收集了它们所需的所有对象之后，遵循计算的数据流模型发出事务。这种收集是通过在事务开始时立即发出对象请求并等待直到事务所需的所有对象到达请求节点来完成的。挑战在于如何调度事务，使得两个关键的性能指标，即（i）提交所有事务的总执行时间，以及（ii）将对象移动到请求节点所涉及的总通信成本最小化。我们在Java中实现了GraphTM，并通过STAMP基准测试套件中的3个微基准测试和5个复杂基准测试，在5种不同的网络拓扑结构上评估了其性能，即团，线，网格，集群和星星，这些网络拓扑结构为实践中常用的一组代表性分布式系统构建了底层通信网络。结果表明，我们的方法的效率和可扩展性。
In this paper, we present GraphTM, an efficient and scalable framework for processing transactions in a distributed environment. The distributed environment is modeled as a graph where each node of the graph is a processing node that issues transactions. The objects that transactions use to execute are also on the graph nodes (the initial placement may be arbitrary). The transactions execute on the nodes which issue them after collecting all the objects that they need following the data-flow model of computation. This collection is done by issuing the requests for the objects as soon as transaction starts and wait until all required objects for the transaction come to the requesting node. The challenge is on how to schedule the transactions so that two crucial performance metrics, namely (i) total execution time to commit all the transactions, and (ii) total communication cost involved in moving the objects to the requesting nodes, are minimized. We implemented GraphTM in Java and assessed its performance through 3 micro-benchmarks and 5 complex benchmarks from STAMP benchmark suite on 5 different network topologies, namely, clique, line, grid, cluster, and star, that make an underlying communication network for a representative set of distributed systems commonly used in practice. The results show the efficiency and scalability of our approach.
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