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Issue No.05 - Sept.-Oct. (2013 vol.17)
pp: 70-73
Abhishek Chandra , University of Minnesota
Jon Weissman , University of Minnesota
Benjamin Heintz , University of Minnesota
ABSTRACT
Cloud computing services are traditionally deployed on centralized computing infrastructures confined to a few data centers, while cloud applications run in a single data center. However, the cloud's centralized nature can be limiting in terms of performance and cost for applications where users, data, and computation are distributed. The authors present an overview of distributed clouds that might be better suited for such applications. They briefly describe the distributed cloud landscape and introduce Nebula, a highly decentralized cloud that uses volunteer edge resources. The authors provide insights into some of its key properties and design issues, and describe a distributed MapReduce application scenario to illustrate the benefits and trade-offs of using distributed and decentralized clouds for distributed data-intensive computing applications.
INDEX TERMS
Distributed databases, Computational modeling, Cloud computing, Computer science, Monitoring, Data models,cloud, distributed systems, data intensive computing
CITATION
Abhishek Chandra, Jon Weissman, Benjamin Heintz, "Decentralized Edge Clouds", IEEE Internet Computing, vol.17, no. 5, pp. 70-73, Sept.-Oct. 2013, doi:10.1109/MIC.2013.93
REFERENCES
1. S. Agarwal et al., “Volley: Automated Data Placement for Geo-Distributed Cloud Services,” Proc. 7th Usenix Conf. Networked Systems Design and Implementation, Usenix Assoc., 2010, pp. 2-2; http://dl.acm.org/citation.cfm?id=1855711.1855713.
2. S. Ramakrishnan et al., “Accelerating Distributed Workflows with Edge Resources,” Proc. 2nd Int'l Workshop on Workflow Models, Systems, Services and Applications in the Cloud (CloudFlow 13), to appear, 2013.
3. M. Ryden et al., Nebula: Data Intensive Computing over Widely Distributed Voluntary Resources,” tech. report TR 13-007, Dept. of Computer Science and Eng., Univ. of Minnesota, Mar. 2013.
4. A. Chandra and J. Weissman, “Nebulas: Using Distributed Voluntary Resources to Build Clouds,” Proc. Workshop Hot Topics in Cloud Computing (HotCloud 09), Usenix Assoc., 2009; www.usenix.org/event/hotcloud09/tech/full_papers/chandra.pdf.
5. J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Proc. 6th Usenix Symp. Operating Systems Design & Implementation (OSDI 04), Usenix Assoc., 2004, pp. 137-149.
6. B. Heintz et al., “Cross-Phase Optimization in MapReduce,” Proc. IEEE Int'l Conf. Cloud Eng. (IC2E 13), IEEE, 2013, pp. 338-347.
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