NDLI: Real-time processing of streaming big data

Content Provider	Springer Nature Link
Author	Safaei, Ali A.
Copyright Year	2016
Abstract	In the era of data explosion, high volume of various data is generated rapidly at each moment of time; and if not processed, the profits of their latent information would be missed. This is the main current challenge of most enterprises and Internet mega-companies (also known as the big data problem). Big data is composed of three dimensions: Volume, Variety, and Velocity. The velocity refers to the high speed, both in data arrival rate (e.g., streaming data) and in data processing (i.e., real-time processing). In this paper, the velocity dimension of big data is concerned; so, real-time processing of streaming big data is addressed in detail. For each real-time system, to be fast is inevitable and a necessary condition (although it is not sufficient and some other concerns e.g., real-time scheduling must be issued, too). Fast processing is achieved by parallelism via the proposed deadline-aware dispatching method. For the other prerequisite of real-time processing (i.e., real-time scheduling of the tasks), a hybrid clustering multiprocessor real-time scheduling algorithm is proposed in which both the partitioning and global real-time scheduling approaches are employed to have better schedulablity and resource utilization, with a tolerable overhead. The other components required for real-time processing of streaming big data are also designed and proposed as real time streaming big data (RT-SBD) processing engine. Its prototype is implemented and experimentally evaluated and compared with the Storm, a well-known real-time streaming big data processing engine. Experimental results show that the proposed RT-SBD significantly outperforms the Storm engine in terms of proportional deadline miss ratio, tuple latency and system throughput.
Starting Page	1
Ending Page	44
Page Count	44
File Format	PDF
ISSN	09226443
Journal	Real-Time Systems
Volume Number	53
Issue Number	1
e-ISSN	15731383
Language	English
Publisher	Springer US
Publisher Date	2016-08-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Streaming big data Hybrid multiprocessor real-time scheduling Clustering Deadline-aware dispatching Periodic continuous queries Computer Systems Organization and Communication Networks Communications Engineering, Networks Special Purpose and Application-Based Systems Performance and Reliability Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Networks and Communications Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications Modeling and Simulation

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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