Workload characterization of the shared/buy-in computing cluster at boston university
Workload characterization of the shared/buy-in computing cluster at boston university
复制标题
波士顿大学共享/购买计算集群的工作负载特征
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Azer Bestavros
中科院分区：
文献类型：
--
作者：
Yonatan Klausner;Christopher Liao;D. Starobinski;Eran Simhon;Azer Bestavros
计算集群为计算研究提供了一个完整的环境，包括生物信息学、机器学习和图像处理。波士顿大学的共享计算集群(SCC)基于共享/买入体系结构，该体系结构结合了共享计算机和买入计算机，共享计算机可供所有用户免费使用，买入计算机是用户购买用于半排他用途的计算机。尽管在表征计算集群的性能方面已经有了大量的工作，但对共享/买入架构知之甚少。利用数据跟踪，对SCC的性能进行了统计分析。我们的结果表明，购买作业的平均等待时间比共享作业的平均等待时间短16.1%。此外，我们还确定了对共享和买入工作经历的绩效有重大影响的参数。这些参数包括并行环境的类型和运行时间限制(即，作业可以使用资源的最长时间)。最后，我们证明了半排他模式，允许任何SCC用户在有限的时间内使用空闲的买入资源，使买入资源的利用率提高了17.4%，从而显著提高了系统的整体性能。
Computing clusters provide a complete environment for computational research, including bio-informatics, machine learning, and image processing. The Shared Computing Cluster (SCC) at Boston University is based on a shared/buy-in architecture that combines shared computers, which are free to be used by all users, and buy-in computers, which are computers purchased by users for semi-exclusive use. Although there exists significant work on characterizing the performance of computing clusters, little is known about shared/buy-in architectures. Using data traces, we statistically analyze the performance of the SCC. Our results show that the average waiting time of a buy-in job is 16.1% shorter than that of a shared job. Furthermore, we identify parameters that have a major impact on the performance experienced by shared and buy-in jobs. These parameters include the type of parallel environment and the run time limit (i.e., the maximum time during which a job can use a resource). Finally, we show that the semi-exclusive paradigm, which allows any SCC user to use idle buy-in resources for a limited time, increases the utilization of buy-in resources by 17.4%, thus significantly improving the performance of the system as a whole.