Virtual Machine Placement Algorithm for Supporting Multiple Applications to Mobile Edge Computing

LI Guanghui; ZHOU Hui; HU Shihong

doi:10.11999/JEIT210415

Volume 44 Issue 7

Jul. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2431-2439

LI Guanghui, ZHOU Hui, HU Shihong. Virtual Machine Placement Algorithm for Supporting Multiple Applications to Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2431-2439. doi: 10.11999/JEIT210415

Citation:

LI Guanghui, ZHOU Hui, HU Shihong. Virtual Machine Placement Algorithm for Supporting Multiple Applications to Mobile Edge Computing[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2431-2439. doi: 10.11999/JEIT210415

Citation:

PDF( 1655 KB)

Virtual Machine Placement Algorithm for Supporting Multiple Applications to Mobile Edge Computing

doi: 10.11999/JEIT210415

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Funds: The National Natural Science Foundation of China (62072216)

Received Date: 2021-05-12
Accepted Date: 2021-10-29
Rev Recd Date: 2021-10-29

Available Online: 2021-12-24

Publish Date: 2022-07-25

Abstract

Abstract

In Mobile Edge Computing (MEC) environment, deploying application services in the form of Virtual Machines (VM) at the edge of the network can effectively reduce the service response delay and reduce the data traffic of core network. There have been many solutions to the problem of optimal allocation of edge network resources, but few studies consider the optimal deployment of VM that provide users with multiple application services to mobile edge networks. To this end, two heuristic algorithms are proposed, Fitness-based Heuristic Placement Algorithm (FHPA) and Divide-and-Conquer Based Heuristic Placement Algorithm (DCBHPA). By distributing VMs that support multiple application services to the MEC network, these two algorithms aim to minimize the data traffic in MEC architecture. Besides, FHPA and DCBHPA define respectively different fitness computing models based on the network connection characteristics of edge servers, as well as the differences in users’ application requests. Thus, VM configuration can be realized through the sub-problem division mechanism. Compared with the baseline algorithms, the simulation results show that the proposed algorithms can better control the system data traffic and improve effectively the utility of edge network service resources.
- Mobile Edge Computing (MEC),
- Virtual Machines (VM) placement,
- Multiple applications,
- Core network traffic

FullText(HTML)

References(21)

References

[1]	JIANG Congfeng, FAN Tiantian, GAO Honghao, et al. Energy aware edge computing: A survey[J]. Computer Communications, 2020, 151: 556–580. doi: 10.1016/J.COMCOM.2020.01.004
[2]	KHAN W Z, AHMED E, HAKAK S, et al. Edge computing: A survey[J]. Future Generation Computer Systems, 2019, 97: 219–235. doi: 10.1016/J.FUTURE.2019.02.050
[3]	PAN Jianli and MCELHANNON J. Future edge cloud and edge computing for internet of things applications[J]. IEEE Internet of Things Journal, 2018, 5(1): 439–449. doi: 10.1109/JIOT.2017.2767608
[4]	ZHOU Yuchen, YU F R, CHEN Jian, et al. Resource allocation for information-centric virtualized heterogeneous networks with in-network caching and mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 11339–11351. doi: 10.1109/TVT.2017.2737028
[5]	SUN Xiang and ANSARI N. EdgeIoT: Mobile edge computing for the internet of things[J]. IEEE Communications Magazine, 2016, 54(12): 22–29. doi: 10.1109/MCOM.2016.1600492CM
[6]	HA K, ABE Y, EISZLER T, et al. You can teach elephants to dance: Agile VM handoff for edge computing[C]. The 2nd ACM/IEEE Symposium on Edge Computing, San Jose, USA, 2017: 12.
[7]	ZHAO Lei and LIU Jiajia. Optimal placement of virtual machines for supporting multiple applications in mobile edge networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7): 6533–6545. doi: 10.1109/TVT.2018.2808171
[8]	GAO Siyi, ZHOU Ao, CHEN Xican, et al. Redundant virtual machine placement in mobile edge computing[C]. The 1st International Conference on Blockchain and Trustworthy Systems, Guangzhou, China, 2019: 371–384.
[9]	SUN Xiang and ANSARI N. Latency aware workload offloading in the cloudlet network[J]. IEEE Communications Letters, 2017, 21(7): 1481–1484. doi: 10.1109/LCOMM.2017.2690678
[10]	KUO J J, YANG H H, and TSAI M J. Optimal approximation algorithm of virtual machine placement for data latency minimization in cloud systems[C]. The IEEE Conference on Computer Communications, Toronto, Canada, 2014: 1303–1311.
[11]	ZENG Deze, GU Lin, GUO Song, et al. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system[J]. IEEE Transactions on Computers, 2016, 65(12): 3702–3712. doi: 10.1109/TC.2016.2536019
[12]	GU Lin, ZENG Deze, GUO Song, et al. Cost efficient resource management in fog computing supported medical cyber-physical system[J]. IEEE Transactions on Emerging Topics in Computing, 2017, 5(1): 108–119. doi: 10.1109/TETC.2015.2508382
[13]	张海波, 刘香渝, 荆昆仑, 等. 车联网中基于NOMA-MEC的卸载策略研究[J]. 电子与信息学报, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017 ZHANG Haibo, LIU Xiangyu, JING Kunlun, et al. Research on NOMA-MEC-based offloading strategy in internet of vehicles[J]. Journal of Electronics &Information Technology, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017
[14]	张海波, 李虎, 陈善学, 等. 超密集网络中基于移动边缘计算的任务卸载和资源优化[J]. 电子与信息学报, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592 ZHANG Haibo, LI Hu, CHEN Shanxue, et al. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1194–1201. doi: 10.11999/JEIT180592
[15]	YANG Song, LI Fan, SHEN Meng, et al. Cloudlet placement and task allocation in mobile edge computing[J]. IEEE Internet of Things Journal, 2019, 6(3): 5853–5863. doi: 10.1109/JIOT.2019.2907605
[16]	AGRYZKOV T, TORTOSA L, VICENT J F, et al. A centrality measure for urban networks based on the eigenvector centrality concept[J]. Environment and Planning B:Urban Analytics and City Science, 2019, 46(4): 668–689. doi: 10.1177/2399808317724444
[17]	WANG Yamin, CAO Zhiying, ZHANG Xiuguo, et al. Clustering-based algorithm for services deployment in mobile edge computing environment[C]. The 2019 IEEE 25th International Conference on Parallel and Distributed Systems, Tianjin, China, 2019: 963–966.
[18]	CHIN T L, CHEN Y S, and LYU K Y. Queuing model based edge placement for work offloading in mobile cloud networks[J]. IEEE Access, 2020, 8: 47295–47303. doi: 10.1109/ACCESS.2020.2979479
[19]	KNIGHT S, NGUYEN H X, FALKNER N, et al. The internet topology zoo[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(9): 1765–1775. doi: 10.1109/JSAC.2011.111002
[20]	ZHAO Lei, LIU Jiajia, SHI Yongpeng, et al. Optimal placement of virtual machines in mobile edge computing[C]. 2017 IEEE Global Communications Conference, Singapore, 2017: 1–6.
[21]	JIA Mike, CAO Jiannong, and LIANG Weifa. Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks[J]. IEEE Transactions on Cloud Computing, 2017, 5(4): 725–737. doi: 10.1109/TCC.2015.2449834