SCHEDULE: NOV 10-16, 2012
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On the Effectiveness of Application-Aware Self-Management for Scientific Discovery in Volunteer Computing Systems
SESSION: Grid Computing
EVENT TYPE: Papers
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Jim Basney
AUTHOR(S):Trilce Estrada, Michela Taufer
ROOM:355-D
ABSTRACT:
An important challenge faced by high-throughput, multiscale applications is that human intervention has a central role in driving their success. However, manual intervention is inefficient, error-prone and promotes resource wasting. This paper presents an application-aware modular framework that provides self-management for computational multiscale applications in volunteer computing (VC). Our framework consists of a learning engine and three modules that can be easily adapted to different distributed systems. The learning engine of this framework is based on our novel tree-like structure called KOTree. KOTree is a fully automatic method that organizes statistical information in a multi-dimensional structure that can be efficiently searched and updated at runtime. Our empirical evaluation shows that our framework can effectively provide application-aware self-management in VC systems. Additionally, we observed that our algorithm is able to predict accurately the expected length of new jobs, resulting in an average of 85% increased throughput with respect to other algorithms.
Chair/Author Details:
Jim Basney (Chair) - National Center for Supercomputing Applications
Trilce Estrada - University of Delaware
Michela Taufer - University of Delaware
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On the Effectiveness of Application-Aware Self-Management for Scientific Discovery in Volunteer Computing Systems
SESSION: Grid Computing
EVENT TYPE:
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Jim Basney
AUTHOR(S):Trilce Estrada, Michela Taufer
ROOM:355-D
ABSTRACT:
An important challenge faced by high-throughput, multiscale applications is that human intervention has a central role in driving their success. However, manual intervention is inefficient, error-prone and promotes resource wasting. This paper presents an application-aware modular framework that provides self-management for computational multiscale applications in volunteer computing (VC). Our framework consists of a learning engine and three modules that can be easily adapted to different distributed systems. The learning engine of this framework is based on our novel tree-like structure called KOTree. KOTree is a fully automatic method that organizes statistical information in a multi-dimensional structure that can be efficiently searched and updated at runtime. Our empirical evaluation shows that our framework can effectively provide application-aware self-management in VC systems. Additionally, we observed that our algorithm is able to predict accurately the expected length of new jobs, resulting in an average of 85% increased throughput with respect to other algorithms.
Chair/Author Details:
Jim Basney (Chair) - National Center for Supercomputing Applications
Trilce Estrada - University of Delaware
Michela Taufer - University of Delaware
Click here to download .ics calendar file