nach oben

The Journal of Supercomputing

Erschienen in:

04.04.2022

Service placement strategy for joint network selection and resource scheduling in edge computing

verfasst von: Junwei Xu, Ruijuan Zheng, Lei Yang, Muhua Liu, Jianqiang Song, Mingchuan Zhang, Qingtao Wu

Erschienen in: The Journal of Supercomputing | Ausgabe 12/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In the edge computing, service placement refers to the process of installing service platforms, databases, and configuration files corresponding to computing tasks on edge service nodes. In order to meet the latency requirements of new types of applications, service placement in edge computing becomes critical. The service placement strategy must be carried out in accordance with the relevant tasks within the program. However, previous research has paid little attention to related tasks within the application. If the service placement strategy does not consider task relevance, the system will frequently switch services and cause serious system overhead. In this paper, we mainly study the problem of service placement in edge computing. At the same time, we considered the issue of network access point selection during data transmission and the dependencies of task execution. We propose a Dynamic Service Placement List Scheduling (DSPLS) algorithm based on dynamic remaining task service time prediction. We conducted relevant simulation experiments, and our algorithm took the least amount of time to complete the task.

Vorheriger Artikel Softwarization and virtualization of VoIP networks

Nächster Artikel An enhanced local outlier detection using random walk on grid information graph

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Wu D, Huang X, Xie X, Nie X, Bao L, Qin Z (2021) Ledge: leveraging edge computing for resilient access management of mobile iot. IEEE Trans Mob Comput 20(3):1110–1125. https://doi.org/10.1109/TMC.2019.2954872CrossRef

Alhaija HA, Mustikovela SK, Mescheder L, Geiger A, Rother C (2017) Augmented reality meets computer vision: efficient data generation for urban driving scenes. Int J Comput Vis 2:1–12

Lai Z, Hu YC, Cui Y, Sun L, Dai N, Lee H-S (2020) Furion: engineering high-quality immersive virtual reality on today’s mobile devices. IEEE Trans Mob Comput 19(7):1586–1602. https://doi.org/10.1109/TMC.2019.2913364CrossRef

Xu W, Song H, Hou L, Zheng H, Zhang X, Zhang C, Hu W, Wang Y, Liu B (2021) Soda: Similar 3d object detection accelerator at network edge for autonomous driving. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pp. 1–10. https://doi.org/10.1109/INFOCOM42981.2021.9488833

Colak I, Bayindir R, Sagiroglu S (2020) The effects of the smart grid system on the national grids. In: 2020 8th International Conference on Smart Grid (icSmartGrid), pp. 122–126. https://doi.org/10.1109/icSmartGrid49881.2020.9144891

Paul PV, Saraswathi R (2017) The internet of things a comprehensive survey. In: 2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), pp. 421–426. https://doi.org/10.1109/ICCPEIC.2017.8290405

Alfonso Velosa HLJFHSRM (2015) Earl Perkins: Predicts 2015: The Internet of Things. https://www.gartner.com/en/documents/2952822

Vestberg H (2018) CEO to shareholders: 50 billion connections 2020. Preprint at https://www.ericsson.com/en/press-releases/2010/4/ceo-to-shareholders-50-billion-connections-2020

Zeng J, Banerjee I, Gensheimer M, Rubin D (2020) Cancer treatment classification with electronic medical health records (student abstract). Proc AAAI Conf Artif Intell 34(10):13981–13982. https://doi.org/10.1609/aaai.v34i10.7263CrossRef

10.

Wan Y, Xu K, Xue G, Wang F (2020) Iotargos: A multi-layer security monitoring system for internet-of-things in smart homes. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pp. 874–883. https://doi.org/10.1109/INFOCOM41043.2020.9155424

11.

Rafique W, Qi L, Yaqoob I, Imran M, Rasool RU, Dou W (2020) Complementing iot services through software defined networking and edge computing: a comprehensive survey. IEEE Commun Surv Tutor 22(3):1761–1804. https://doi.org/10.1109/COMST.2020.2997475CrossRef

12.

Liang Y, Ge J, Zhang S, Niu C, Song W, Luo B (2020) Efficient service entity chain placement in mobile edge computing. In: 2020 16th International Conference on Mobility, Sensing and Networking (MSN), pp. 182–189. https://doi.org/10.1109/MSN50589.2020.00042

13.

Zhang Y, He J, Guo S (2018) Energy-efficient dynamic task offloading for energy harvesting mobile cloud computing. In: 2018 IEEE International Conference on Networking, Architecture and Storage (NAS), pp. 1–4. https://doi.org/10.1109/NAS.2018.8515736

14.

Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D (2017) On multi-access edge computing: a survey of the emerging 5g network edge cloud architecture and orchestration. IEEE Commun Surv Tutor 19(3):1657–1681. https://doi.org/10.1109/COMST.2017.2705720CrossRef

15.

Zhao G, Xu H, Zhao Y, Qiao C, Huang L (2020) Offloading dependent tasks in mobile edge computing with service caching. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pp. 1997–2006. https://doi.org/10.1109/INFOCOM41043.2020.9155396

16.

Phan TK, Rocha M, Griffin D, Rio M (2018) Utilitarian placement of composite services. IEEE Trans Netw Serv Manag 15(2):638–649. https://doi.org/10.1109/TNSM.2018.2798413CrossRef

17.

Mosa A, Sakellariou R (2019) Dynamic virtual machine placement considering cpu and memory resource requirements. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 196–198. https://doi.org/10.1109/CLOUD.2019.00042

18.

Yu Y (2016) Mobile edge computing towards 5g: Vision, recent progress, and open challenges. China Commun 13(Supplement2):89–99. https://doi.org/10.1109/CC.2016.7833463CrossRef

19.

Poularakis K, Llorca J, Tulino A.M, Taylor I, Tassiulas L (2019) Joint service placement and request routing in multi-cell mobile edge computing networks. In: IEEE INFOCOM 2019—IEEE Conference on Computer Communications, pp. 10–18. https://doi.org/10.1109/INFOCOM.2019.8737385

20.

Garcia-Saavedra A, Iosifidis G, Costa-Perez X, Leith DJ (2018) Joint optimization of edge computing architectures and radio access networks. IEEE J Sel Areas Commun 36(11):2433–2443. https://doi.org/10.1109/JSAC.2018.2874142CrossRef

21.

Chaaban A, Maier H, Sezgin A, Mathar R (2016) Three-way channels with multiple unicast sessions: Capacity approximation via network transformation. IEEE Trans Inf Theory 62(12):7086–7102. https://doi.org/10.1109/TIT.2016.2614318MathSciNetCrossRefMATH

22.

Loghin D, Ramapantulu L, Teo Y.M(2017) On understanding time, energy and cost performance of wimpy heterogeneous systems for edge computing. In: 2017 IEEE International Conference on Edge Computing (EDGE), pp. 1–8. https://doi.org/10.1109/IEEE.EDGE.2017.10

23.

Horner, L.J.: Edge strategies in industry: Overview and challenges. IEEE Transactions on Network and Service Management, 1–1 (2021). https://doi.org/10.1109/TNSM.2021.3092940

24.

Arabnejad H, Barbosa J (2012) Fairness resource sharing for dynamic workflow scheduling on heterogeneous systems. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 633–639. https://doi.org/10.1109/ISPA.2012.94

25.

Xie G, Zeng G, Li R, Li K (2017) Energy-aware processor merging algorithms for deadline constrained parallel applications in heterogeneous cloud computing. IEEE Trans Sustain Comput 2(2):62–75. https://doi.org/10.1109/TSUSC.2017.2705183CrossRef

26.

Piao J.T, Yan J (2010) A network-aware virtual machine placement and migration approach in cloud computing. In: 2010 Ninth International Conference on Grid and Cloud Computing, pp. 87–92. https://doi.org/10.1109/GCC.2010.29

27.

Beraldi R, Mtibaa A, Alnuweiri H (2017) Cooperative load balancing scheme for edge computing resources. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 94–100. https://doi.org/10.1109/FMEC.2017.7946414

28.

Alasmari KR, Green RC, Alam M (2018) Mobile edge offloading using markov decision processes. In: Liu S, Tekinerdogan B, Aoyama M, Zhang L-J (eds) Edge Computing - EDGE 2018. Springer, Cham, pp 80–90

29.

Ouyang T, Li R, Chen X, Zhou Z, Tang X (2019) Adaptive user-managed service placement for mobile edge computing: An online learning approach. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 1468–1476. https://doi.org/10.1109/INFOCOM.2019.8737560

30.

Fan Y, Tao L, Chen J (2019) Associated task scheduling based on dynamic finish time prediction for cloud computing. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 2005–2014. https://doi.org/10.1109/ICDCS.2019.00198

31.

Farhadi V, Mehmeti F, He T, Porta T.L, Khamfroush H, Wang S, Chan K.S. (2019)Service placement and request scheduling for data-intensive applications in edge clouds. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 1279–1287. https://doi.org/10.1109/INFOCOM.2019.8737368

32.

Gao B, Zhou Z, Liu F, Xu F (2019) Winning at the starting line: Joint network selection and service placement for mobile edge computing. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 1459–1467. https://doi.org/10.1109/INFOCOM.2019.8737543

33.

Borst S, Gupta V, Walid A (2010) Distributed caching algorithms for content distribution networks. In: 2010 Proceedings IEEE INFOCOM, pp. 1–9. https://doi.org/10.1109/INFCOM.2010.5461964

34.

Giang N.K, Blackstock M, Lea R, Leung V.C.M (2015) Developing iot applications in the fog: A distributed dataflow approach. In: 2015 5th International Conference on the Internet of Things (IOT), pp. 155–162. https://doi.org/10.1109/IOT.2015.7356560

35.

Gupta H, Vahid Dastjerdi A, Ghosh S.K, Buyya R (2016) ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience 47(1)

36.

Huang H, Zhang H, Guo T, Guo J, He C (2019) Reliable redundant services placement in federated micro-clouds. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), pp. 446–453. https://doi.org/10.1109/ICPADS47876.2019.00070

37.

Bo MA, Zahle TU (1977) Scheduling according to job priority with prevention of deadlock and permanent blocking. Acta Informatica 8(2):153–175MathSciNetCrossRef

38.

Peha J.M, Tobagi F.A (1990) Evaluating scheduling algorithms for traffic with heterogeneous performance objectives. Evaluating scheduling algorithms for traffic with heterogeneous performance objectives

39.

Kaur R, Singh G (2012) Genetic algorithm solution for scheduling jobs in multiprocessor environment. In: 2012 Annual IEEE India Conference (INDICON), pp. 968–973. https://doi.org/10.1109/INDCON.2012.6420757

40.

Wang G, Guo H, Wang Y (2015) A novel heterogeneous scheduling algorithm with improved task priority. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 1826–1831. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.48

41.

Munir E.U, Mohsin S, Hussain A, Nisar M.W, Ali S (2013) Sdbats: A novel algorithm for task scheduling in heterogeneous computing systems. In: 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum, pp. 43–53

42.

Yang P, Liu J, Chen C, Ding Y, Deng C, Zeng Z (2020) An efficient low delay task scheduling algorithm based on ant colony system in heterogeneous environments. In: 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 519–524. https://doi.org/10.1109/HPCC-SmartCity-DSS50907.2020.00064

43.

Pujolle G (2020) Virtualization. In: Software Networks: Virtualization, SDN, 5G, and Security, pp. 1–12. https://doi.org/10.1002/9781119694748.ch1

44.

Wu J, Wong E.W.M, Chan Y.-C, Zukerman M (2017) Energy efficiency-qos tradeoff in cellular networks with base-station sleeping. In: GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1–7. https://doi.org/10.1109/GLOCOM.2017.8254042

45.

Lau T.L, Tsang E.P.K (1998) The guided genetic algorithm and its application to the generalized assignment problem. In: Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294), pp. 336–343. https://doi.org/10.1109/TAI.1998.744862

46.

PhuLai: EUA Datasets. https://github.com/swinedge/eua-dataset (2020)

47.

Cordeiro D, Mouni G, Perarnau S, Trystram D, Vincent J.-M, Wagner F (2010) Random graph generation for scheduling simulations. In: Proceedings of 3rd International ICST Conference on Simulation Tools and Techniques

48.

Nannicini S, Pecorella T (1998) Performance evaluation of polling protocols for data transmission on wireless communication networks. In: ICUPC ’98. IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384), vol. 2, pp 1241–12452

49.

Krapivsky PL, Redner S, Leyvraz F (2000) Connectivity of growing random networks. Phys Rev Lett 85(21):4629–4632CrossRef

50.

Jain R (1992) A comparison of hashing schemes for address lookup in computer networks. IEEE Trans Commun 40(10):1570–1573CrossRef

Titel: Service placement strategy for joint network selection and resource scheduling in edge computing
verfasst von: Junwei Xu
Ruijuan Zheng
Lei Yang
Muhua Liu
Jianqiang Song
Mingchuan Zhang
Qingtao Wu
Publikationsdatum: 04.04.2022
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 12/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-04458-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 12/2022

Formal method for the synthesis of optimal topologies of computing systems based on the projective description of graphs

A Shapley value-based thermal-efficient workload distribution in heterogeneous data centers

An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation

Multi-context unsupervised domain adaption for HEp-2 cell classification using maximum partial classifier discrepancy

Edge computing and the internet of things on agricultural green productivity

Credit card fraud detection using a deep learning multistage model

Premium Partner