Topology-Aware Resource Allocation for IoT Services in Clouds
With the development of the Internet of Things (IoT), the cloud data centers have already been an important foundation to support IoT data analysis and data-driven IoT services. For the datadriven services provision, cloud resources are necessary for the service components in the form of virtual mac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8554258/ |
| _version_ | 1819133481856270336 |
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| author | Xin Li Zhen Lian Xiaolin Qin Wu Jie |
| author_facet | Xin Li Zhen Lian Xiaolin Qin Wu Jie |
| author_sort | Xin Li |
| collection | DOAJ |
| description | With the development of the Internet of Things (IoT), the cloud data centers have already been an important foundation to support IoT data analysis and data-driven IoT services. For the datadriven services provision, cloud resources are necessary for the service components in the form of virtual machines (VMs). At the same time, there is a frequent data transmission among the service components (or VMs). Hence, to reduce the IoT services' response time, it is critical to improve the network issue and avoid network bottleneck during resource allocation. In this paper, we investigate the VM placement problem for balanced network utilization by avoiding network congestion. We first use the resource topology model to represent user requests and formulate the problem formally. We prove that the problem is NP-hard and present a heuristic algorithm based on the resource topologies. The core idea is to analyze the global and required resource topologies and place the required VMs into multiple servers with lower communication cost. We conduct extensive simulations, and the simulation results show that our algorithms have significant performance improvement on reducing network occupation and IoT service delay compared to the best-fit strategy and divide-and-conquer strategy. |
| first_indexed | 2024-12-22T09:47:59Z |
| format | Article |
| id | doaj.art-8f260151a42046f3aa3a8e23428ec2e5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:47:59Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8f260151a42046f3aa3a8e23428ec2e52022-12-21T18:30:29ZengIEEEIEEE Access2169-35362018-01-016778807788910.1109/ACCESS.2018.28842518554258Topology-Aware Resource Allocation for IoT Services in CloudsXin Li0https://orcid.org/0000-0002-1450-9241Zhen Lian1Xiaolin Qin2Wu Jie3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCenter for Networked Computing, Temple University, Philadelphia, PA, USAWith the development of the Internet of Things (IoT), the cloud data centers have already been an important foundation to support IoT data analysis and data-driven IoT services. For the datadriven services provision, cloud resources are necessary for the service components in the form of virtual machines (VMs). At the same time, there is a frequent data transmission among the service components (or VMs). Hence, to reduce the IoT services' response time, it is critical to improve the network issue and avoid network bottleneck during resource allocation. In this paper, we investigate the VM placement problem for balanced network utilization by avoiding network congestion. We first use the resource topology model to represent user requests and formulate the problem formally. We prove that the problem is NP-hard and present a heuristic algorithm based on the resource topologies. The core idea is to analyze the global and required resource topologies and place the required VMs into multiple servers with lower communication cost. We conduct extensive simulations, and the simulation results show that our algorithms have significant performance improvement on reducing network occupation and IoT service delay compared to the best-fit strategy and divide-and-conquer strategy.https://ieeexplore.ieee.org/document/8554258/Cloud data centergraph theoryIoT servicenetwork optimizationvirtual machine placement |
| spellingShingle | Xin Li Zhen Lian Xiaolin Qin Wu Jie Topology-Aware Resource Allocation for IoT Services in Clouds IEEE Access Cloud data center graph theory IoT service network optimization virtual machine placement |
| title | Topology-Aware Resource Allocation for IoT Services in Clouds |
| title_full | Topology-Aware Resource Allocation for IoT Services in Clouds |
| title_fullStr | Topology-Aware Resource Allocation for IoT Services in Clouds |
| title_full_unstemmed | Topology-Aware Resource Allocation for IoT Services in Clouds |
| title_short | Topology-Aware Resource Allocation for IoT Services in Clouds |
| title_sort | topology aware resource allocation for iot services in clouds |
| topic | Cloud data center graph theory IoT service network optimization virtual machine placement |
| url | https://ieeexplore.ieee.org/document/8554258/ |
| work_keys_str_mv | AT xinli topologyawareresourceallocationforiotservicesinclouds AT zhenlian topologyawareresourceallocationforiotservicesinclouds AT xiaolinqin topologyawareresourceallocationforiotservicesinclouds AT wujie topologyawareresourceallocationforiotservicesinclouds |