More Web Proxy on the site http://driver.im/

research-article

Reveal: : Robustness-aware VNF placement and request scheduling in edge clouds

Authors:

Haibo WangAuthors Info & Claims

Volume 233, Issue C

https://doi.org/10.1016/j.comnet.2023.109882

Published: 01 September 2023 Publication History

Abstract

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal–dual based algorithm with a competitive ratio of 1 − ϵ, O ( log 1 / ϵ ) where ϵ ∈ ( 0, 1 ). Experiment and simulation results show that Reveal can achieve better performance and robustness than other alternatives.

References

[1]

L. Tong, Y. Li, W. Gao, A hierarchical edge cloud architecture for mobile computing, in: IEEE INFOCOM 2016, pp. 1–9.

[2]

Pan J., McElhannon J., Future edge cloud and edge computing for internet of things applications, IEEE Internet Things J. 5 (1) (2017) 439–449.

[3]

Lyu X., Tian H., Ni W., Zhang Y., Zhang P., Liu R.P., Energy-efficient admission of delay-sensitive tasks for mobile edge computing, IEEE Trans. Commun. 66 (6) (2018) 2603–2616.

[4]

Wang Y., Zhang Y., Sheng M., Guo K., On the interaction of video caching and retrieving in multi-server mobile-edge computing systems, IEEE Wirel. Commun. Lett. 8 (5) (2019) 1444–1447.

[5]

L. Liu, M. Gruteser, EdgeSharing: Edge Assisted Real-time Localization and Object Sharing in Urban Streets, in: IEEE INFOCOM 2021.

[6]

M.E. Computing, Deployment of Mobile Edge Computing in an NFV Environment, Vol. 17, ETSI Group Report MEC, 2018, p. V1.

[7]

T. Ouyang, R. Li, X. Chen, Z. Zhou, X. Tang, Adaptive user-managed service placement for mobile edge computing: An online learning approach, in: IEEE INFOCOM 2019, pp. 1468–1476.

[8]

J. Xu, L. Chen, P. Zhou, Joint service caching and task offloading for mobile edge computing in dense networks, in: IEEE INFOCOM 2018, pp. 207–215.

[9]

S. Agarwal, F. Malandrino, C.-F. Chiasserini, S. De, Joint VNF placement and CPU allocation in 5G, in: IEEE INFOCOM 2018, pp. 1943–1951.

[10]

L. Gu, D. Zeng, J. Hu, B. Li, H. Jin, Layer Aware Microservice Placement and Request Scheduling at the Edge, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–9, https://doi.org/10.1109/INFOCOM42981.2021.9488779.

[11]

Poularakis K., Llorca J., Tulino A.M., Taylor I., Tassiulas L., Service placement and request routing in MEC networks with storage, computation, and communication constraints, IEEE/ACM Trans. Netw. 28 (3) (2020) 1047–1060.

Digital Library

[12]

H. Li, L. Wang, Online orchestration of cooperative defense against DDoS attacks for 5G MEC, in: 2018 IEEE Wireless Communications and Networking Conference, WCNC.

[13]

Dao N.-N., Phan T.V., Sa’ad U., Kim J., Bauschert T., Do D.-T., Cho S., Securing heterogeneous iot with intelligent ddos attack behavior learning, IEEE Syst. J. (2021).

[14]

J. Hou, P. Fu, Z. Cao, A. Xu, Machine Learning Based DDos Detection Through NetFlow Analysis, in: MILCOM 2018 - 2018 IEEE Military Communications Conference, MILCOM, pp. 1–6, https://doi.org/10.1109/MILCOM.2018.8599738.

[15]

He T., Ciftcioglu E.N., Wang S., Chan K.S., Location privacy in mobile edge clouds: A chaff-based approach, IEEE J. Sel. Areas Commun. 35 (11) (2017) 2625–2636,.

[16]

R. Potharaju, N. Jain, Demystifying the dark side of the middle: A field study of middlebox failures in datacenters, in: Proceedings of the 2013 Conference on Internet Measurement Conference, 2013, pp. 9–22.

[17]

Jia, C. Yang, Reliability-aware Dynamic Service Chain Scheduling in 5G Networks based on Reinforcement Learning, in: IEEE INFOCOM 2021.

[18]

Delimitrou C., Kozyrakis C., Bolt: I know what you did last summer... in the cloud, ACM SIGARCH Comput. Archit. News 45 (1) (2017) 599–613.

[19]

Wilkes J., Google Cluster-Usage Traces v3, Google Inc, Mountain View, CA, USA, 2020.

[20]

M. Masdari, M. Jalali, A survey and taxonomy of DoS attacks in cloud computing, Secur. Commun. Netw. 9 (16) 3724–3751.

[21]

Sohal A.S., Sandhu R., Sood S.K., Chang V., A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments, Comput. Secur. 74 (2018) 340–354.

Digital Library

[22]

A. Randazzo, I. Tinnirello, Kata Containers: An Emerging Architecture for Enabling MEC Services in Fast and Secure Way, in: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security, IOTSMS, pp. 209–214.

[23]

Bagdasaryan E., Veit A., Hua Y., Estrin D., Shmatikov V., How to backdoor federated learning, in: International Conference on Artificial Intelligence and Statistics, PMLR, 2020, pp. 2938–2948.

[24]

S. Ntalampiras, M. Fiore, Forecasting mobile service demands for anticipatory MEC, in: 2018 IEEE 19th International Symposium on” a World of Wireless, Mobile and Multimedia Networks”(WoWMoM), pp. 14–19.

[25]

Pelle I., Paolucci F., Sonkoly B., Cugini F., Latency-sensitive edge/cloud serverless dynamic deployment over telemetry-based packet-optical network, IEEE J. Sel. Areas Commun. 39 (9) (2021) 2849–2863.

[26]

Guo S., Dai Y., Xu S., Qiu X., Qi F., Trusted cloud-edge network resource management: DRL-driven service function chain orchestration for IoT, IEEE Internet Things J. 7 (7) (2019) 6010–6022.

[27]

Duc T.L., Leiva R.G., Casari P., Östberg P.-O., Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey, ACM Comput. Surv. 52 (5) (2019) 1–39.

[28]

Toka L., Dobreff G., Fodor B., Sonkoly B., Machine learning-based scaling management for kubernetes edge clusters, IEEE Trans. Netw. Serv. Manag. 18 (1) (2021) 958–972.

Digital Library

[29]

Xu X., Fang Z., Qi L., Zhang X., He Q., Zhou X., Tripres: Traffic flow prediction driven resource reservation for multimedia iov with edge computing, ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 17 (2) (2021) 1–21.

[30]

Chen M., Miao Y., Gharavi H., Hu L., Humar I., Intelligent traffic adaptive resource allocation for edge computing-based 5G networks, IEEE Trans. Cogn. Commun. Netw. 6 (2) (2019) 499–508.

[31]

Furno A., Naboulsi D., Stanica R., Fiore M., Mobile demand profiling for cellular cognitive networking, IEEE Trans. Mob. Comput. 16 (3) (2016) 772–786.

[32]

Ha K., Abe Y., Chen Z., Hu W., Amos B., Pillai P., Satyanarayanan M., Adaptive VM Handoff Across Cloudlets, Computer Science Department, Carnegie Mellon University, 2015.

[33]

Xu Z., Liang W., Xu W., Jia M., Guo S., Efficient algorithms for capacitated cloudlet placements, IEEE Trans. Parallel Distrib. Syst. 27 (10) (2015) 2866–2880.

[34]

Ceselli A., Premoli M., Secci S., Mobile edge cloud network design optimization, IEEE/ACM Trans. Netw. 25 (3) (2017) 1818–1831.

[35]

Shmoys D.B., Tardos É., An approximation algorithm for the generalized assignment problem, Math. Program. 62 (1) (1993) 461–474.

[36]

Xu H., Li X.-Y., Huang L., Deng H., Huang H., Wang H., Incremental deployment and throughput maximization routing for a hybrid SDN, IEEE/ACM Trans. Netw. 25 (3) (2017) 1861–1875.

[37]

Chen L.-C., Choi H.-A., Approximation algorithms for data distribution with load balancing of web servers, in: Cluster, Vol. 1, Citeseer, 2001, p. 274.

[38]

Mitzenmacher M., Upfal E., Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis, Cambridge University Press, 2017.

[39]

PuLP, 2022, URL https://pypi.org/project/PuLP/. (Accessed 14 February 2022).

[40]

Zhao G., Xu H., Chen S., Huang L., Wang P., Joint optimization of flow table and group table for default paths in SDNs, IEEE/ACM Trans. Netw. (2018).

[41]

L. Guo, J. Pang, A. Walid, Joint placement and routing of network function chains in data centers, in: IEEE INFOCOM 2018, pp. 612–620.

[42]

tcping, 2022, URL https://www.elifulkerson.com/projects/tcping.php. (Accessed 14 February 2022).

[43]

iperf, 2022, URL https://iperf.fr/. (Accessed 14 February 2022).

[44]

Farhadi V., Mehmeti F., He T., La Porta T.F., Khamfroush H., Wang S., Chan K.S., Poularakis K., Service placement and request scheduling for data-intensive applications in edge clouds, IEEE/ACM Trans. Netw. 29 (2) (2021) 779–792.

Digital Library

[45]

Shang X., Huang Y., Liu Z., Yang Y., Reducing the service function chain backup cost over the edge and cloud by a self-adapting scheme, IEEE Trans. Mob. Comput. 21 (8) (2021) 2994–3008.

[46]

Shanmugam K., Golrezaei N., Dimakis A.G., Molisch A.F., Caire G., Femtocaching: Wireless content delivery through distributed caching helpers, IEEE Trans. Inform. Theory 59 (12) (2013) 8402–8413.

Digital Library

[47]

hping, 2022, URL http://hping.org/. (Accessed 14 February 2022).

[48]

stress, 2022, URL https://linux.die.net/man/1/stress. (Accessed 14 February 2022).

[49]

He T., Khamfroush H., Wang S., La Porta T., Stein S., It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources, in: 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS, IEEE, 2018, pp. 365–375.

[50]

Wang S., Urgaonkar R., Zafer M., He T., Chan K., Leung K.K., Dynamic service migration in mobile edge-clouds, in: 2015 IFIP Networking Conference, IFIP Networking, 2015,.

[51]

Yala L., Frangoudis P.A., Ksentini A., Latency and availability driven VNF placement in a MEC-NFV environment, in: 2018 IEEE Global Communications Conference, GLOBECOM, IEEE, 2018, pp. 1–7.

[52]

Cziva R., Anagnostopoulos C., Pezaros D.P., Dynamic, latency-optimal vNF placement at the network edge, in: IEEE Infocom 2018-Ieee Conference on Computer Communications, IEEE, 2018, pp. 693–701.

[53]

Zhao G., Xu H., Zhao Y., Qiao C., Huang L., Offloading tasks with dependency and service caching in mobile edge computing, IEEE Transactions on Parallel and Distributed Systems 32 (11) (2021) 2777–2792,.

[54]

Xu Z., Zhang Z., Lui J.C., Liang W., Xia Q., Zhou P., Xu W., Wu G., Affinity-aware VNF placement in mobile edge clouds via leveraging GPUs, IEEE Trans. Comput. 70 (12) (2020) 2234–2248.

[55]

Yue Y., Cheng B., Wang M., Li B., Liu X., Chen J., Throughput optimization and delay guarantee VNF placement for mapping SFC requests in NFV-enabled networks, IEEE Trans. Netw. Serv. Manag. 18 (4) (2021) 4247–4262,.

[56]

Yang S., Li F., Trajanovski S., Chen X., Wang Y., Fu X., Delay-aware virtual network function placement and routing in edge clouds, IEEE Trans. Mob. Comput. 20 (2) (2019) 445–459.

Digital Library

[57]

Zhou R., Wang N., Huang Y., Pang J., Chen H., DPS: Dynamic pricing and scheduling for distributed machine learning jobs in edge-cloud networks, IEEE Trans. Mob. Comput. (2022).

[58]

Qu L., Assi C., Shaban K., Khabbaz M.J., A reliability-aware network service chain provisioning with delay guarantees in NFV-enabled enterprise datacenter networks, IEEE Trans. Netw. Serv. Manag. 14 (3) (2017) 554–568.

Digital Library

[59]

Qu L., Assi C., Khabbaz M.J., Ye Y., Reliability-aware service function chaining with function decomposition and multipath routing, IEEE Trans. Netw. Serv. Manag. 17 (2) (2019) 835–848.

[60]

Wang Y., Zhang L., Yu P., Chen K., Qiu X., Meng L., Kadoch M., Cheriet M., Reliability-oriented and resource-efficient service function chain construction and backup, IEEE Trans. Netw. Serv. Manag. 18 (1) (2020) 240–257.

[61]

Wang M., Cheng B., Chen J., Joint availability guarantee and resource optimization of virtual network function placement in data center networks, IEEE Trans. Netw. Serv. Manag. 17 (2) (2020) 821–834.

[62]

Tu H., Zhao G., Xu H., Zhao Y., Qiu Y., Huang L., RoNS: Robust network function services in clouds, Comput. Netw. (ISSN ) 215 (2022) 109212,. https://www.sciencedirect.com/science/article/pii/S1389128622002961.

Digital Library

[63]

Sairam R., Bhunia S.S., Thangavelu V., Gurusamy M., NETRA: Enhancing IoT security using NFV-based edge traffic analysis, IEEE Sens. J. (2019),.

[64]

Du W., Li A., Zhou P., Niu B., Wu D., Privacyeye: A privacy-preserving and computationally efficient deep learning-based mobile video analytics system, IEEE Trans. Mob. Comput. 21 (9) (2021) 3263–3279.

[65]

Wang H., Xu H., Huang H., Chen M., Chen S., Robust task offloading in dynamic edge computing, IEEE Trans. Mob. Comput. (2021).

[66]

Mingshi W., Junqin L., Tianxiang H., Pingping W., Kang Y., Jiakai H., Diwen Z., Yang Y., Riming T., Failure prediction based VNF migration mechanism for multimedia services in power grid substation monitoring, in: 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, IEEE, 2022, pp. 1–6.

[67]

Alharbi T., Aljuhani A., Liu H., Holistic DDoS mitigation using NFV, in: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, CCWC, 2017,.

[68]

Nawab F., Wedgechain: A trusted edge-cloud store with asynchronous (lazy) trust, in: 2021 IEEE 37th International Conference on Data Engineering, ICDE, IEEE, 2021, pp. 408–419.

[69]

He T., Ciftcioglu E.N., Wang S., Chan K.S., Location privacy in mobile edge clouds: A chaff-based approach, IEEE J. Sel. Areas Commun. 35 (11) (2017) 2625–2636.

[70]

Li T., Hu S., Beirami A., Smith V., Ditto: Fair and robust federated learning through personalization, in: International Conference on Machine Learning, PMLR, 2021, pp. 6357–6368.

[71]

Li J., Liang W., Huang M., Jia X., Reliability-aware network service provisioning in mobile edge-cloud networks, IEEE Trans. Parallel Distrib. Syst. 31 (7) (2020) 1545–1558.

Cited By

Du AJia JChen JGuo LWang X(2024)Online two-timescale service placement for time-sensitive applications in MEC-assisted networkComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110339244:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110339

Index Terms

Reveal: Robustness-aware VNF placement and request scheduling in edge clouds
1. Networks
  1. Network algorithms
  2. Network services
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory

Index terms have been assigned to the content through auto-classification.

Recommendations

Multiple-resource request scheduling for differentiated QoS at website gateway

Differentiated quality of service is a way for a website operator to provide different service levels to its clients. Traditional HTTP request scheduling schemes can achieve this, but they schedule requests to manage only one server resource, such as ...
Multiple-Resource Request Scheduling for Differentiated QoS at Website Gateway
AINA '08: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications

Differentiated quality of service is a way for a website operator to provide different service levels to its clients. Traditional HTTP request scheduling schemes can achieve this, but they schedule requests to manage only one server resource, such as ...
Towards an efficient VNF placement in network function virtualization
Abstract
Network Function Virtualization (NFV) decouples network function (also called middlebox function) software from specified appliances onto general shared servers. Thus, it has been being regarded as a promising technology to overcome ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 233, Issue C

Sep 2023

441 pages

ISSN:1389-1286

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 September 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Du AJia JChen JGuo LWang X(2024)Online two-timescale service placement for time-sensitive applications in MEC-assisted networkComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110339244:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110339

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents