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Application-aware Task Scheduling in Heterogeneous Edge Cloud
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Publication Year
2019-10-01
Journal
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, pp.1316-1320
Keyword
application-awareedge cloud architectureedge computingheterogeneousInternet of Thingslatency-awarelatency-tolerantQoStask scheduling
Mesh Keyword
Edge cloudsheterogeneousLatency-awarelatency-tolerantTask-scheduling
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsComputer Science ApplicationsInformation Systems and ManagementManagement of Technology and InnovationSafety, Risk, Reliability and QualityMedia TechnologyControl and Optimization
Abstract
As the acceleration gaining for utilizing edge computing in various IoT applications, the demands of effective task scheduling algorithms are rising alarmingly. Some of the real-time applications (e.g., self-driving cars, AR/VR apps) requires real-time responses. On the other hand, for the applications (such as deep learning algorithms and neural networks) demand powerful edge resources. However, most of the studies only focus on low latency improvement and lacks to provide efficient task scheduling. As a result, edge computing paradigm requires a new approach to deal with different applications. In this paper, we propose an adaptive application-aware task scheduling algorithm for running over heterogeneous edge cloud. The proposed scheduling algorithm provides not only the QoS of the applications but also increases the performance of the overall scheduling and utility of edge resources. We conduct an extensive experimental study to show the efficiency of our algorithm. From this research, we improve the overall performance of the task scheduling, considering both task heterogeneity and edge heterogeneity and to maximize the edge resource utilization effectively.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36448
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078233034&origin=inward
DOI
https://doi.org/10.1109/ictc46691.2019.8939927
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8932631
Type
Conference
Funding
VI. ACKNOWLEDGEMENT This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018-0-01431) supervised by the IITP(Institute for Information communications Technology Promotion)This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018- 0-01431)
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Ko, Young-Bae고영배
Department of Software and Computer Engineering
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