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Sector and Sphere: the design and implementation of a high-performance data cloud.
Yunhong Gu1, Robert L Grossman
1University of Illinois at Chicago, Chicago, IL 60607, USA.
Summary
Sector and Sphere clouds offer efficient, distributed data processing. Sector cloud is twice as fast as Hadoop for large-scale data sorting, demonstrating improved cloud computing performance.
Area of Science:
- Computer Science
- Distributed Systems
- Cloud Computing
Background:
- Cloud computing enables processing large datasets on commodity clusters with appropriate models.
- Existing storage and compute clouds have limitations in managing geographically distributed data.
Purpose of the Study:
- To introduce the Sector storage cloud and Sphere compute cloud.
- To enable distributed data management and user-defined functions across data centers.
- To compare the performance of Sector/Sphere with Hadoop.
Main Methods:
- Design and implementation of Sector storage cloud for distributed data management.
- Development of Sphere compute cloud supporting user-defined functions (UDFs) across data centers.
- Experimental comparison using the Terasort benchmark against Hadoop.
Main Results:
- Sector cloud manages data within and across geographically distributed data centers.
- Sphere compute cloud supports UDFs for data processing both locally and remotely.
- Sector demonstrated approximately twice the performance of Hadoop in Terasort benchmark tests.
Conclusions:
- Sector/Sphere provides a robust and high-performance solution for distributed cloud computing.
- The system offers enhanced capabilities for managing and processing large datasets across multiple data centers.
- Sector/Sphere is available as open-source software, promoting wider adoption and development.
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