An edge cloud–based body data sensing architecture for artificial intelligence computation
As various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present a...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-04-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719839014 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547831716511744 |
|---|---|
| author | TaeYoung Kim JongBeom Lim |
| author_facet | TaeYoung Kim JongBeom Lim |
| author_sort | TaeYoung Kim |
| collection | DOAJ |
| description | As various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present an edge cloud–based body data sensing architecture for artificial intelligence computation. The main rationale for designing the edge cloud–based sensing architecture is as follows. By analyzing physical body data on the edge cloud computing system, we can identify the relationship between body activities and health conditions for persons. In addition, we can support real-time applications without catastrophic failures by our efficient and stable implementation of the sensing architecture. Our cloud storage architecture is designed to support both stateful and stateless applications, which are compatible with traditional infrastructures and provide server consolidation with a CPU-aware scheduling of virtual machines. Performance results show that our edge cloud–based architecture outperforms the previous architecture in terms of failures, processing time, and scalability. |
| format | Article |
| id | doaj-art-2dd34f92c8914a72944a34a183252ff9 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2dd34f92c8914a72944a34a183252ff92025-02-03T06:43:06ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-04-011510.1177/1550147719839014An edge cloud–based body data sensing architecture for artificial intelligence computationTaeYoung Kim0JongBeom Lim1College of Education, Hankuk University of Foreign Studies, Seoul, KoreaDepartment of Game and Multimedia Engineering, Korea Polytechnic University, Siheung-si, KoreaAs various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present an edge cloud–based body data sensing architecture for artificial intelligence computation. The main rationale for designing the edge cloud–based sensing architecture is as follows. By analyzing physical body data on the edge cloud computing system, we can identify the relationship between body activities and health conditions for persons. In addition, we can support real-time applications without catastrophic failures by our efficient and stable implementation of the sensing architecture. Our cloud storage architecture is designed to support both stateful and stateless applications, which are compatible with traditional infrastructures and provide server consolidation with a CPU-aware scheduling of virtual machines. Performance results show that our edge cloud–based architecture outperforms the previous architecture in terms of failures, processing time, and scalability.https://doi.org/10.1177/1550147719839014 |
| spellingShingle | TaeYoung Kim JongBeom Lim An edge cloud–based body data sensing architecture for artificial intelligence computation International Journal of Distributed Sensor Networks |
| title | An edge cloud–based body data sensing architecture for artificial intelligence computation |
| title_full | An edge cloud–based body data sensing architecture for artificial intelligence computation |
| title_fullStr | An edge cloud–based body data sensing architecture for artificial intelligence computation |
| title_full_unstemmed | An edge cloud–based body data sensing architecture for artificial intelligence computation |
| title_short | An edge cloud–based body data sensing architecture for artificial intelligence computation |
| title_sort | edge cloud based body data sensing architecture for artificial intelligence computation |
| url | https://doi.org/10.1177/1550147719839014 |
| work_keys_str_mv | AT taeyoungkim anedgecloudbasedbodydatasensingarchitectureforartificialintelligencecomputation AT jongbeomlim anedgecloudbasedbodydatasensingarchitectureforartificialintelligencecomputation AT taeyoungkim edgecloudbasedbodydatasensingarchitectureforartificialintelligencecomputation AT jongbeomlim edgecloudbasedbodydatasensingarchitectureforartificialintelligencecomputation |