SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

doi:10.23919/JSEE.2024.000092

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (4): 906-921.doi: 10.23919/JSEE.2024.000092

• SYSTEMS ENGINEERING • Previous Articles

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

Yingchao HAN¹(), Weixiao MENG¹(), Wentao FAN²^,*()

¹ School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
² China Mobile (Suzhou) Software Technology Company Limited, Suzhou 215000, China

Received:2024-01-19 Online:2024-08-18 Published:2024-08-06
Contact: Wentao FAN E-mail:21B90567@stu.hit.edu.cn;wxmeng@hit.edu.cn;fanwentao@cmss.chinamobile.com
About author:
HAN Yingchao was born in 1987. He received his M.S. degree from Harbin Institute of Technology, Harbin, China, in 2012. He is pursuing his Ph.D. degree at the School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China. His research interests include design of the aircraft and networking communication. E-mail: 21B90567@stu.hit.edu.cn

MENG Weixiao was born in 1968. He received his B.S. degree in electronic instruments and measurement technology and Ph.D. degree in information and communication engineering from Harbin Institute of Technology, Harbin, China, in 1990 and 2000 respectively. He is now a professor in the School of Electronics and Information Engineering, Harbin Institute of Technology. His research interests include wireless communication and networking, sky and ground information transmission and networking, and integrated communication perception. E-mail: wxmeng@hit.edu.cn

FAN Wentao was born in 1995. He received his B.S. and Ph.D. degrees of information and communication engineering from Beijing University of Posts and Telecommunications, Beijing, China, in 2017 and 2023 respectively. His research interests include cloud/edge computing, network orchestration, data center network, and remote direct memory access (RDMA) system. E-mail: fanwentao@cmss.chinamobile.com
Supported by:
This work was supported by the Key Research and Development (R&D) Plan of Heilongjiang Province of China (JD22A001).

Abstract

Abstract:

With the continuous development of network functions virtualization (NFV) and software-defined networking (SDN) technologies and the explosive growth of network traffic, the requirement for computing resources in the network has risen sharply. Due to the high cost of edge computing resources, coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge. In this paper, we focus on optimizing the placement of network services in cloud-edge environments to maximize the efficiency. It is first proved that, in cloud-edge environments, placing one service function chain (SFC) integrally in the cloud or at the edge can improve the utilization efficiency of edge resources. Then a virtual network function (VNF) performance-resource (P-R) function is proposed to represent the relationship between the VNF instance computing performance and the allocated computing resource. To select the SFCs that are most suitable to deploy at the edge, a VNF placement and resource allocation model is built to configure each VNF with its particular P-R function. Moreover, a heuristic recursive algorithm is designed called the recursive algorithm for max edge throughput (RMET) to solve the model. Through simulations on two scenarios, it is verified that RMET can improve the utilization efficiency of edge computing resources.

Key words: cloud-edge environment, virtual network function (VNF) performance-resource (P-R) function, edge resource allocation

Yingchao HAN, Weixiao MENG, Wentao FAN. SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment[J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 906-921.

Figures/Tables 13

Fig 1

Fig 2

Fig 3

Table 1

Fig 4

Table 2

Table 3

Fig 5

Fig 6

Fig 7

Table 4

Table 5

Fig 8

References 31

1	COLAKOVIC A, HADZIALIC M Internet of things (IOT): a review of enabling technologies, challenges, and open research issues. Computer Networks, 2018, 144, 17- 39. doi: 10.1016/j.comnet.2018.07.017
2	PANTELOPOULOS A, BOURBAKIS N G A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. on Systems, Man, and Cybernetics, Part C, 2009, 40 (1): 1- 12.
3	YURTSEVER E, LAMBERT J, CARBALLO A, et al A survey of autonomous driving: common practices and emerging technologies. IEEE Access, 2020, 8, 58443- 58469. doi: 10.1109/ACCESS.2020.2983149
4	ALTURKI R, GAY V. Augmented and virtual reality in mobile fitness applications: a survey. Cham: Springer, 2019.
5	FENG C, HAN P C, ZHANG X, et al Computation offloading in mobile edge computing networks: a survey. Journal of Network and Computer Applications, 2022, 202, 103366. doi: 10.1016/j.jnca.2022.103366
6	CAO K, HU S Y, SHI Y, et al A survey on edge and edge-cloud computing assisted cyber-physical systems. IEEE Trans. on Industrial Informatics, 2021, 17 (11): 7806- 7819. doi: 10.1109/TII.2021.3073066
7	ETSI GS NFV 001. Network functions virtualisation (NFV); use cases. Nice: European Telecommunications Standards Institute, 2013.
8	RFC 7665. Service function chaining (SFC) architecture. Lake Wylie: RFC Editor, 2015.
9	MCKEOWN N, ANDERSON T, BALAKRISHNAN H, et al OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 2008, 38 (2): 69- 74. doi: 10.1145/1355734.1355746
10	MCKEOWN N Software-defined networking. INFOCOM Keynote Talk, 2009, 17 (2): 30- 32.
11	ZHANG S L An overview of network slicing for 5G. IEEE Wireless Communications, 2019, 26 (3): 111- 117. doi: 10.1109/MWC.2019.1800234
12	DESOUSA N F S, PEREZ D A L, ROSA R V, et al Network service orchestration: a survey. Computer Communications, 2019, 142/143, 69- 94.
13	SHI W S, CAO J, ZHANG Q, et al Edge computing: vision and challenges. IEEE Internet of Things Journal, 2016, 3 (5): 637- 646. doi: 10.1109/JIOT.2016.2579198
14	REN J, ZHANG D Y, HE S W, et al A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Computing Surveys, 2019, 52 (6): 1- 36.
15	WANG K, ZHOU Q H, GUO S, et al Cluster frameworks for efficient scheduling and resource allocation in data center networks: a survey. IEEE Communications Surveys & Tutorials, 2018, 20 (4): 3560- 3580.
16	DU M Z, WANG Y, YE K J, et al Algorithmics of cost-driven computation offloading in the edge-cloud environment. IEEE Trans. on Computers, 2020, 69 (10): 1519- 1532. doi: 10.1109/TC.2020.2976996
17	HUANG M F, LIU W, WANG T, et al A cloud–MEC collaborative task offloading scheme with service orchestration. IEEE Internet of Things Journal, 2019, 7 (7): 5792- 5805.
18	LONG X, WU J G, CHEN L. Energy-efficient offloading in mobile edge computing with edge-cloud collaboration. Proc. of the International Conference on Algorithms and Architectures for Parallel Processing, 2018: 460−475.
19	SLIM F, GUILLEMIN F, HADJADJ-AOUL Y. CLOSE: a costless service offloading strategy for distributed edge cloud. Proc. of the 15th IEEE Annual Consumer Communications & Networking Conference, 2018. DOI: 10.1109/CCNC.2018.8319276.
20	PENG G, WU H M, WU H, et al Constrained multiobjective optimization for IoT-enabled computation offloading in collaborative edge and cloud computing. IEEE Internet of Things Journal, 2021, 8 (17): 13723- 13736. doi: 10.1109/JIOT.2021.3067732
21	WU J Z, CAO Z Y, ZHANG Y J, et al. Edge-cloud collaborative computation offloading model based on improved partical swarm optimization in MEC. Proc. of the IEEE 25th International Conference on Parallel and Distributed Systems, 2019: 959−962.
22	ELIE E H, NGUYEN T M, ASSI C Joint optimization of computational cost and devices energy for task offloading in multi-tier edge-clouds. IEEE Trans. on Communications, 2019, 67 (5): 3407- 3421. doi: 10.1109/TCOMM.2019.2895040
23	ZHANG Q X, LIU F M, ZENG C B. Adaptive interference-aware VNF placement for service-customized 5G network slices. Proc. of the IEEE Conference on Computer Communications, 2019: 2449−2457.
24	RANDRIAMASINORO N M, NGUYEN K K, CHERIET M. Optimized resource allocation in edge-cloud environment. Proc. of the Annual IEEE International Systems Conference, 2018. DOI: 10.1109/SYSCON.2018.8369606.
25	SEFRAOUI O, AISSAOUI M, ELEULDJ M OpenStack: toward an open-source solution for cloud computing. International Journal of Computer Applications, 2012, 55 (3): 38- 42. doi: 10.5120/8738-2991
26	AGARWAL S, MALANDRINO F, CHIASSERINI C F, et al VNF placement and resource allocation for the support of vertical services in 5G networks. IEEE/ACM Transactions on networking, 2019, 27 (1): 433- 446. doi: 10.1109/TNET.2018.2890631
27	ALAMEDDINE H A, SHARAFEDDINE S, SEBBAH S, et al Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing. IEEE Journal on Selected Areas in Communications, 2019, 37 (3): 668- 682. doi: 10.1109/JSAC.2019.2894306
28	LI M, ZHANG Q X, LIU F M. Finedge: a dynamic cost-efficient edge resource management platform for NFV network. Proc. of the IEEE/ACM 28th International Symposium on Quality of Service, 2020. DOI: 10.1109/IWQoS49365.2020.9212908.
29	VAN ROSSEM S, TAVERNIER W, COLLE D, et al Profile-based resource allocation for virtualized network functions. IEEE Trans. on Network and Service Management, 2019, 16 (4): 1374- 1388. doi: 10.1109/TNSM.2019.2943779
30	HU X, CAO W, ZHU H Q. Data plane development kit (DPDK). Florida: CRC Press, 2020.
31	FAN W T, YANG F, WANG P L, et al DRL-based service function chain edge-to-edge and edge-to-cloud joint offloading in edge-cloud network. IEEE Trans. on Network and Service Management, 2023, 20 (4): 4478- 4493. doi: 10.1109/TNSM.2023.3271769

Notation	Description
$ G(S,L) $	Network topology
$ S={S}_{c}+{S}_{e} $	Set of servers, where $ {S}_{c} $ is the set of cloud servers, and $ {S}_{e} $ is the set of edge servers
$ L $	Set of links. $ l(u,v)\in L $ represents the link between server $ u $, $ v $ $ (u,v\in S) $
$ F $	Set of VNF types
$ C $	Set of SFCs
$ {k}_{s} $	Computing capacity of server $ s $, $ s\in S $
$ {k}_{s}^{r} $	Remaining computing resource of server $ s $, $ s\in S $
$ {b}_{l}(u,v) $	Bandwidth of link $ l(u,v) $
$ \psi (f,s) $	Computing resource assigned to $ f $-type VNF instance in server $ s $
$ \tau (u,v) $	Transmission delay of link $ l(u,v) $
$ {f}_{c}\left(i\right) $	The $ i{\mathrm{th}} $ VNF type in SFC $ c $, $ {f}_{c}\left(i\right)\in F $
$ {p}_{c}\left(i\right) $	Server in which $ {f}_{c}\left(i\right) $ is deployed
$ {\alpha }_{f} $	Slope of $ f $-type VNF throughput-resource function
$ {\beta }_{f} $	Intercept of the horizontal axis of $ f $-type throughput-resource function
$ {\gamma }_{f} $	Exponent of $ f $-type VNF delay-resource function
$ < {\lambda }_{c}{D}_{c}^{{\mathrm{QoS}}} $>	Service requirement of SFC $ c $, where $ {\lambda }_{c} $ is the peak flowrate of $ c $, and $ {D}_{c}^{{\mathrm{QoS}}} $ is the delay $ {\mathrm{QoS}} $ of $ c $
$ \delta \left(c\right) $	0 if SFC $ c $ is deployed in the cloud, and 1 if $ c $ is deployed at the edge

f-Type	$ {\alpha }_{f} $	$ {\beta }_{f} $	$ {\gamma }_{f} $
1	10	0	−2
2	8	0	−4
3	12	0	−8
4	3	1	−3
5	0.5	10	−1
6	5	8	−6
7	2	2	−9
8	2	5	−7
9	0.5	0.5	−3
10	6	0	−5

VNF	f-Type	$ {\alpha }_{f} $	$ {\beta }_{f} $	$ {\gamma }_{f} $
FW	1	150	0	−10
TM	2	100	0	−8
NAT	3	80	0	−8
ADNF1	4	7	0	−5
ADNF2	5	5	5	−3
ADNF3	6	10	0	−3
ADNF4	7	15	5	−5
ADNF5	8	0.6	0	−2
ADNF6	9	12	0	−5

SFC	Flowrate/(request/s)	$ {D}^{{\mathrm{QoS}}}/{\mathrm{ms}} $
SFC1	10	1
SFC2	8	2
SFC3	7	12
SFC4	6	12
SFC5	5	15
SFC6	1	20

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 13

References 31

Related Articles 0

Recommended Articles

Metrics

Comments

SFC	Flowrate/(request/s)	$ {D}^{{\mathrm{QoS}}} /{\mathrm{ms}}$
SFC1	55	20
SFC2	50	15
SFC3	45	10
SFC4	45	30
SFC5	35	20
SFC6	30	10
SFC7	25	15
SFC8	20	30
SFC9	15	25
SFC10	15	15