Emalayan Vairavanathan
NetApp
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Publication
Featured researches published by Emalayan Vairavanathan.
cluster computing and the grid | 2012
Emalayan Vairavanathan; Samer Al-Kiswany; Lauro Beltrão Costa; Zhao Zhang; Daniel S. Katz; Michael Wilde; Matei Ripeanu
This paper evaluates the potential gains a workflow-aware storage system can bring. Two observations make us believe such storage system is crucial to efficiently support workflow-based applications: First, workflows generate irregular and application-dependent data access patterns. These patterns render existing storage systems unable to harness all optimization opportunities as this often requires conflicting optimization options or even conflicting design decision at the level of the storage system. Second, when scheduling, workflow runtime engines make suboptimal decisions as they lack detailed data location information. This paper discusses the feasibility, and evaluates the potential performance benefits brought by, building a workflow-aware storage system that supports per-file access optimizations and exposes data location. To this end, this paper presents approaches to determine the application-specific data access patterns, and evaluates experimentally the performance gains of a workflow-aware storage approach. Our evaluation using synthetic benchmarks shows that a workflow-aware storage system can bring significant performance gains: up to 7× performance gain compared to the distributed storage system - MosaStore and up to 16× compared to a central, well provisioned, NFS server.
grid computing | 2015
Lauro Beltrão Costa; Hao Yang; Emalayan Vairavanathan; Abmar Barros; Ketan Maheshwari; Gilles Fedak; Daniel S. Katz; Michael Wilde; Matei Ripeanu; Samer Al-Kiswany
This article evaluates the potential gains a workflow-aware storage system can bring. Two observations make us believe such storage system is crucial to efficiently support workflow-based applications: First, workflows generate irregular and application-dependent data access patterns. These patterns render existing generic storage systems unable to harness all optimization opportunities as this often requires enabling conflicting optimizations or even conflicting design decisions at the storage system level. Second, most workflow runtime engines make suboptimal scheduling decisions as they lack the detailed data location information that is generally hidden by the storage system. This paper presents a limit study that evaluates the potential gains from building a workflow-aware storage system that supports per-file access optimizations and exposes data location. Our evaluation using synthetic benchmarks and real applications shows that a workflow-aware storage system can bring significant performance gains: up to 3x performance gains compared to a vanilla distributed storage system deployed on the same resources yet unaware of the possible file-level optimizations.
Archive | 2015
Dheeraj Raghavender Sangamkar; Ajay Bakre; Vladimir Avram; Emalayan Vairavanathan; Viswanath Chandrasekara Bharathi
arXiv: Distributed, Parallel, and Cluster Computing | 2013
Samer Al-Kiswany; Emalayan Vairavanathan; Lauro Beltrão Costa; Hao Yang; Matei Ripeanu
Archive | 2017
Ajay Bakre; Vishnu Vardhan Chandra Kumaran; Alvin Lam; Emalayan Vairavanathan; Viswanath Chandrasekara Bharathi; Vladimir Avram; Dheeraj Raghavender Sangamkar; Oliver Seiler; Carmen Lum
Archive | 2017
Dheeraj Raghavender Sangamkar; Song Guen Yoon; Emalayan Vairavanathan; Yi Zhang
Future Generation Computer Systems | 2017
Samer Al-Kiswany; Lauro Beltrão Costa; Hao Yang; Emalayan Vairavanathan; Matei Ripeanu
Archive | 2017
Ajay Bakre; Vishnu Vardhan Chandra Kumaran; Alvin Lam; Emalayan Vairavanathan; Viswanath Chandrasekara Bharathi; Vladimir Avram; Dheeraj Raghavender Sangamkar; Oliver Seiler; Carmen Lum
Archive | 2017
Vivek Venkatesan; Alvin Lam; Varun Ganesh; Emalayan Vairavanathan
Archive | 2017
Tymoteusz Altman; Yi Zhang; Dheeraj Raghavender Sangamkar; Emalayan Vairavanathan