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Comparison of hardware/software layered architectures of distributed
computing paradigms: cluster computing, peer-to-peer (P2P) computing, grid computing, cloud computing. |
Authors:Liu, Bingwei; Chen, Yu; Hadiks, Ari; Blasch, Erik; Aved, Alex; Chen, Genshe
Department of Electrical and Computer Engineering, Binghamton University, SUNY, Binghamton, NY
Abstract
Information fusion utilizes a collection of data sources for uncertainty reduction, coverage extension, and situation awareness. Future information fusion solutions require systems design
- coordination with users
- metrics of performance
- and methods of multilevel security
- A current trend that can enable all of these services is cloud computing. Cloud computing as defined by NIST is: Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
- Cloud computing provides capabilities (on-demand self service, broad network access, resource pooling, rapid elasticity, and measured service) over different types of clouds (private, community, public, and hybrid).
Published in:Aerospace and Electronic Systems Magazine, IEEE (Volume:29, Issue: 10)
As mobile cloud computing facilitates a wide spectrum of smart applications, the need for fusing various types of data available in the cloud grows rapidly. In particular, social and sensor data lie at the core in such applications, but typically processed separately. This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice - a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependent people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.
Date:
01 Aug 2013
Recent advances in cloud computing pose interesting capabilities for information fusion which have similar requirements of big data computations. With a cloud enabled environment, information fusion systems could be conducted over vast amounts of entities across multiple databases. In order to properly implement information fusion in a cloud, information management, system design, and real-time execution must be considered. In this chapter, three aspects of current developments integrating low/high-level information fusion (LLIF/HLIF) and cloud computing are discussed:
- agent-based service architectures,
- ontologies, and
- metrics (timeliness, confidence, and security).
We introduce the Cloud-Enabled Bayes Network (CEBN) for wide area motion imagery target tracking and identification. The Google Fusion Tables service is also selected as a case study to illustrate commercial cloud-based information fusion applications.
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