Affiliation:
1. Southwest University, China
2. Telecom SudParis, France
Abstract
Many problems in science and engineering are usually emulated as a set of mutually interacting models, resulting in a coupled or multiphysics application. These component models show challenges originating from their interdisciplinary nature and from their computational and algorithmic complexities. In general, these models are independently developed and maintained, so that they commonly employ the global file system for exchanging their data in the coupled application.
To effectively use the local file cache on the compute node for exchanging the data among the processes of such applications, and consequently boosting I/O performance, this article presents a novel mechanism to migrate a process from one compute node to another node on the basis of block I/O dependency. In this newly proposed mechanism, the block I/O dependency between two involved processes running on the different nodes is profiled as block access similarity by taking advantage of the
Cohen’s kappa statistic
. Then, the process is supposed to be dynamically migrated from its source node to the destination node, on which there is another process having heavy block I/O dependency. As a result, both processes can exchange their data by utilizing the local file cache instead of the global file system to reduce I/O time. The experimental results demonstrate that the I/O performance can be significantly improved, and the time required for executing the application can be resultantly decreased, as expected.
Funder
the Fundamental Research Funds for the Central Universities
Hunan Provincial Natural Science Foundation of China
the Opening Project of State Key Laboratory for Novel Software Technology
Publisher
Association for Computing Machinery (ACM)
Subject
Software,Computer Science (miscellaneous),Control and Systems Engineering
Cited by
1 articles.
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1. Toward Efficient Block Replication Management in Distributed Storage;ACM Transactions on Modeling and Performance Evaluation of Computing Systems;2020-09-30