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Service-oriented Task Scheduling in Heterogeneous Edge Cloud for Internet-of-Things
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Young-Bae Ko | - |
| dc.contributor.author | THANDA OO | - |
| dc.date.accessioned | 2022-11-29T02:32:02Z | - |
| dc.date.available | 2022-11-29T02:32:02Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29524 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19531 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2020. 2 | - |
| dc.description.tableofcontents | 1. Introduction 1 2.Related Work 5 3.Service-oriented Task Scheduling Scheme 9 3.1System Assumption of Edge Cloud Architecture 9 3.2Heterogeneity of Edge Cloud Architecture 11 3.3Proposed Scheme 12 3.4Service-oriented Task Scheduling Algorithm 14 4.Performance Evaluation 18 4.1Simulation Environment 18 4.2Simulation Result 21 5.Conclusion 28 References 30 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Service-oriented Task Scheduling in Heterogeneous Edge Cloud for Internet-of-Things | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1138584 | - |
| dc.identifier.uci | I804:41038-000000029524 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029524 | - |
| dc.subject.keyword | QoS | - |
| dc.subject.keyword | edge computing | - |
| dc.description.alternativeAbstract | 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 and mission-critical applications (e.g., self-driving cars, AR/VR apps) require real-time responses. On the other hand, 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, the edge computing paradigm requires a new approach to deal with different types of applications. In this paper, we propose an adaptive service-oriented 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. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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