Abstract
The proliferation of smart devices and the advancement of data-intensive services has led to explosion of data, which uncovers massive opportunities as well as challenges related to real-time analysis of big data streams. The edge computing frameworks implemented over manycore systems can be considered as a promising solution to address these challenges. However, in spite of the availability of modern computing systems with a large number of processing cores and high memory capacity, the performance and scalability of manycore systems can be limited by the software and operating system (OS) level bottlenecks. In this work, we focus on these challenges, and discuss how accelerated communication, efficient caching, and high performance computation can be provisioned over manycore systems. The proposed Fast, ExteNsible, and ConsolidatEd (FENCE) framework leverages the availability of a large number of computing cores and overcomes the OS level bottlenecks to provide high performance and scalability for intelligent big data processing. We implemented a prototype of FENCE and the experiment results demonstrate that FENCE provides improved data reception throughput, read/write throughput, and application processing performance as compared to the baseline Linux system.
Highlights
The amount of data being generated by users, applications and devices is increasing at a staggering rate, and 90 percent of the data in the world was generated in the last few years [1]
The main contributions of this paper can be summarized as follows: 1) we analyzed the major performance and scalability bottlenecks in monolithic operating system (OS) such as Linux; 2) we discussed several key enabling technologies that can be used to overcome the performance bottlenecks and provide optimized performance and scalability; 3) we introduced the FENCE framework for achieving high performance and scalability for intelligent big data processing over manycore systems; and 4) we presented an evolving graph processing use-case to demonstrate the performance of FENCE
The massive amount of data being generated in the internet of things (IoT) era needs to be harnessed and processed effectively to provide intelligent data-driven solutions
Summary
The amount of data being generated by users, applications and devices is increasing at a staggering rate, and 90 percent of the data in the world was generated in the last few years [1]. Emerging IoT applications impose significant challenges for current computing architectures [6] Such applications are resource-hungry and data-intensive and require real-time analysis, context and location awareness, increased security, and bandwidth efficiency. These requirements can partially be fulfilled by employing scalable network infrastructures including advanced network equipment, intelligent networking applications, and advanced radio access technologies. As IoT evolves from consumers to industrial sectors, the growing size and complexity of data make its real-time collection, processing, and analysis even more challenging The rest of paper is organized as follows: Section II presents the motivation of FENCE; Section III describes the details of FENCE framework and its key enabling technologies; Section IV demonstrates the performance of FENCE in terms of data reception, storage, and application processing performance, followed by Section V that concludes the paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
