ROMA: Resource Orchestration for Microservices-based 5G Applications

被引:4
作者
Gholami, Anousheh [1 ,4 ]
Rao, Kunal [2 ]
Hsiung, Wang-Pin [3 ]
Po, Oliver [3 ]
Sankaradas, Murugan [2 ]
Chakradhar, Srimat [2 ]
机构
[1] Univ Maryland, College Pk, MD 20742 USA
[2] NEC Labs Amer, Princeton, NJ USA
[3] NEC Labs Amer, San Jose, CA USA
[4] NEC Labs Amer Inc, Princeton, NJ USA
来源
PROCEEDINGS OF THE IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2022 | 2022年
关键词
resource orchestration; IoT; 5G; edge computing; microservices; system modelling and optimization;
D O I
10.1109/NOMS54207.2022.9789821
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the growth of 5G, Internet of Things (IoT), edge computing and cloud computing technologies, the infrastructure (compute and network) available to emerging applications (AR/VR, autonomous driving, industry 4.0, etc.) has become quite complex. There are multiple tiers of computing (IoT devices, near edge, far edge, cloud, etc.) that are connected with different types of networking technologies (LAN, LTE, 5G, MAN, WAN, etc.). Deployment and management of applications in such an environment is quite challenging. In this paper, we propose ROMA, which performs resource orchestration for microservices-based 5G applications in a dynamic, heterogeneous, multi-tiered compute and network fabric. We assume that only application-level requirements are known, and the detailed requirements of the individual microservices in the application are not specified. As part of our solution, ROMA identifies and leverages the coupling relationship between compute and network usage for various microservices and solves an optimization problem in order to appropriately identify how each microservice should be deployed in the complex, multi-tiered compute and network fabric, so that the end-to-end application requirements are optimally met. We implemented two real-world 5G applications in video surveillance and intelligent transportation system (ITS) domains. Through extensive experiments, we show that ROMA is able to save up to 90%, 55% and 44% compute and up to 80%, 95% and 75% network bandwidth for the surveillance (watchlist) and transportation application (person and car detection), respectively. This improvement is achieved while honoring the application performance requirements, and it is over an alternative scheme that employs a static and overprovisioned resource allocation strategy by ignoring the resource coupling relationships.
引用
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页数:9
相关论文
共 19 条
[1]  
Al-Tarawneh MAB, 2021, INT J ADV COMPUT SC, V12, P304
[2]  
[Anonymous], 2021, 3GPP TS 23.501 V17.2.0
[3]  
[Anonymous], GLPK GNU LIN PROGR K
[4]  
celestrak, About Us
[5]  
D'Oro Salvatore, 2020, Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, P1, DOI 10.1145/3397166.3409133
[6]   eXP-RAN-An Emulator for Gaining Experience With Radio Access Networks, Edge Computing, and Slicing [J].
Esper, Joao Paulo ;
Abdallah, Abdallah S. ;
Clayman, Stuart ;
Moreira, Waldir ;
Oliveira-Jr, Antonio ;
Correa, Sand Luz ;
Cardoso, Kleber Vieira .
IEEE ACCESS, 2020, 8 :152975-152989
[7]   Mobile cloud computing: A survey [J].
Fernando, Niroshinie ;
Loke, Seng W. ;
Rahayu, Wenny .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2013, 29 (01) :84-106
[8]  
FFmpeg, 2024, A complete, cross-platform solution to record, convert and stream audio and video
[9]   Collaborative Cloud-Edge-Local Computation Offloading for Multi-Component Applications [J].
Gholami, Anousheh ;
Baras, John S. .
2021 ACM/IEEE 6TH SYMPOSIUM ON EDGE COMPUTING (SEC 2021), 2021, :361-365
[10]  
Kubernetes, Production-Grade Container Orchestration