Misco: A System for Data Analysis Applications on Networks of Smartphones Using MapReduce

Kakantousis, Theofilos; Boutsis, Ioannis; Kalogeraki, Vana; Gunopulos, Dimitrios; Gasparis, Giorgos; Dou, Adam

doi:10.1109/mdm.2012.75

Cited by 15 publications

(9 citation statements)

References 5 publications

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“…At the same time we have to look at distributed cloud computing [5] approaches by which we can access shared and distributed resources over the distributed cloud environment. The term distributed database refers to the data which is distributed among so many clients are users.…”

Section: Benefits Of Data Migrationmentioning

confidence: 99%

Improving the Data Transmission Speed in Cloud Migration by Using Mapreduce for Bigdata

Naresh¹,

P²,

Vempati³

et al. 2020

IJETMS

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Cloud acts as a data storage and also used for data transfer from one cloud to other. Here data exchange takes place among cloud centers of organizations. At each cloud center huge amount of data was stored, which interns hard to store and retrieve information from it. While migrating the data there are some issues like low data transfer rate, end to end latency issues and data storage issues will occur. As data was distributed among so many cloud centers from single source, will reduces the speed of migration. In distributed cloud computing it is very difficult to transfer the data fast and securely. This paper explores MapReduce within the distributed cloud architecture where MapReduce assists at each cloud. It strengthens the data migration process with the help of HDFS. Compared to existing cloud migration approach the proposed approach gives accurate results interns of speed, time and efficiency.

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Section: Benefits Of Data Migrationmentioning

confidence: 99%

Improving the Data Transmission Speed in Cloud Migration by Using Mapreduce for Bigdata

Naresh¹,

P²,

Vempati³

et al. 2020

IJETMS

View full text Add to dashboard Cite

show abstract

“…There is a need for a more lightweight file system which can adequately address dynamic network topology concerns. Another MapReduce framework based on Python, Misco [13] was implemented on Nokia mobile phones. It has a similar server-client model where the server keeps track of various user jobs and assigns them to workers on demand.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Hadoop MapReduce for Mobile Clouds

George

Chen

Stoleru

et al. 2019

IEEE Trans. Cloud Comput.

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Abstract-The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile network enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access, when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The MDFS (Mobile Distributed File System) [1] addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment.

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“…Another MapReduce framework, Misco [14] was implemented on Nokia smartphones. It has a server-client model, similar to Hyrax, where the server keeps track of various user jobs and assigns them to workers on demand.…”

Section: B State Of Art and Research Challengesmentioning

confidence: 99%

Hadoop MapReduce for Tactical Clouds

George

Chen

Stoleru

et al. 2014

2014 IEEE 3rd International Conference on Cloud Networking (CloudNet)

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We envision a future where real-time computation on the battlefield provides the tactical advantage to an Army over its adversary. The ability to collect and process large amounts of data to provide actionable information to soldiers will greatly enhance their situational awareness. Our vision is based on the observation that the U.S. Military is attempting to equip soldiers with smartphones. While individual phones may not be sufficiently powerful for processing large amount of data, using the mobile devices carried by a squad or platoon of Soldiers as a single distributed computing platform, a Tactical Cloud, would enable large-scale data processing to be conducted in battlefields. In order for this vision to be realized, two issues have to be addressed. The first is the complexity of writing applications for distributed computing environments, and the second is the vulnerability of data on mobile devices. In this paper, we propose combining two existing technologies to address these issues. The first is Hadoop MapReduce, a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware, and the second is the Mobile Distributed File System (MDFS) which allows distributed data storage with built-in reliability and security. By making the MDFS file system work with Hadoop on mobile devices, we hope to enable big data applications on tactical clouds.

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Misco: A System for Data Analysis Applications on Networks of Smartphones Using MapReduce

Cited by 15 publications

References 5 publications

Improving the Data Transmission Speed in Cloud Migration by Using Mapreduce for Bigdata

Improving the Data Transmission Speed in Cloud Migration by Using Mapreduce for Bigdata

Hadoop MapReduce for Mobile Clouds

Hadoop MapReduce for Tactical Clouds

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