nach oben

Cluster Computing

Erschienen in:

19.09.2017

Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud

verfasst von: Li Chunlin, Tang Jianhang, Luo Youlong

Erschienen in: Cluster Computing | Ausgabe 2/2018

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

In the paper, the distributed scheduling optimization model for resource-intensive mobile application is proposed. Lagrangian method is applied to achieve distributed scheduling optimization in hybrid cloud. By decomposing the Kuhn–Tucker conditions into different roles of mobile user, public cloud supplier and local cloud supplier, the scheduling optimization problem in hybrid cloud is converted into a distributed problem. The system design and example of distributed scheduling optimization for resource intensive mobile application is also given. The local or public cloud provider uses service-level agreement (SLA) in determining the share of resources to be allocated to the mobile user. The distributed scheduling optimization algorithm for resource intensive mobile application is proposed, which includes three parts: local cloud agent scheduling optimization, public cloud service scheduling and mobile application QoS optimization. The experiments study how data size, request arrival rate, number of mobile users and mobility have effect on the proposed algorithm and other related works.

Vorheriger Artikel An empirical evaluation of energy-aware load balancing technique for cloud data center

Nächster Artikel Agent-based fuzzy constraint-directed negotiation for service level agreements in cloud computing

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

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

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"

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

Kovachev, D., Cao, Y., Klamma, R.: Building mobile multimedia services: a hybrid cloud computing approach. Multimed. Tools Appl. 70(2), 977–1005 (2014)CrossRef

Cole, Y., Zhang, H., Ge, L., et al.: ScanMe mobile: a local and cloud hybrid service for analyzing apks. Res. Adapt. Converg. Syst. RACS 2015, 268–273 (2015)

Khalifa, A., Azab, M., Eltoweissy, M.: Resilient hybrid Mobile Ad-hoc cloud over collaborating heterogeneous nodes. In: 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom, pp. 134–143 (2014)

Kazi, R., Deters, R.A.: Cloud-hosted hybrid framework for consuming Web Services on mobile devices. In: 2013 International Conference on Selected Topics in Mobile and Wireless Networking, MoWNeT, pp. 106–111 (2013)

Skourletopoulos, G., Mavromoustakis, C.X., Mastorakis, G., et al.: An evaluation of cloud-based mobile services with limited capacity: a linear approach. Soft Comput. 21, 4523–4530 (2016)

Bourdena, A., Mavromoustakis, C.X., Mastorakis, G., et al.: Using socio-spatial context in mobile cloud process offloading for energy conservation in wireless devices. IEEE Trans. Cloud Comput. 99, 1 (2015)

Cerviño, J., Rodríguez, P., Trajkovska, I., et al.: A cost-effective methodology applied to videoconference services over hybrid clouds. Mob. Netw. Appl. 18(1), 103–109 (2013)CrossRef

Reiter, A., Zefferer, T.: Flexible and secure resource sharing for mobile augmentation systems. In: 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud, pp. 31–40 (2016)

Farris, I., Militano, L., Nitti, M., et al.: Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments. In: 2nd IEEE World Forum on Internet of Things. WF-IoT, vol. 2015, pp. 591–596 (2015)

10.

Mavromoustakis, C.X., Andreou, A., Mastorakis, G., et al.: On the performance evaluation of a novel offloading-based energy conservation mechanism for wireless devices. In: 6th International ICST Conference on Mobile Networks and Management, MONAMI, pp. 179–191 (2014)

11.

Viswanathan, H., Lee, E.K., Rodero, I., et al.: Uncertainty-aware autonomic resource provisioning for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26(8), 2363–2372 (2015)CrossRef

12.

Viswanathan, H., Pandey, P., Pompili, D.: Maestro: orchestrating concurrent application workflows in mobile device clouds. In: 2016 13th IEEE International Conference on Autonomic Computing, ICAC, pp. 257–262 (2016)

13.

Shakkeera, L., Tamilselvan, L.: Energy-aware application scheduling and consolidation in mobile cloud computing with load balancing, emerging research in computing, information, communication and applications. Springer, Berlin, pp. 253–264 (2016)

14.

Rashidi, S., Sharifian, S.: A hybrid heuristic queue based algorithm for task assignment in mobile cloud. Future Gener. Comput. Syst. 68, 331–345 (2017)

15.

Care, R., Hassan, H.A.H., Suárez, L., et al.: Energy-efficient scheduling for cloud mobile gaming. IEEE Globecom Workshops. GC Wkshps 2014, 1198–1204 (2014)

16.

Barbarossa, S., Sardellitti, S., Di Lorenzo, P.: Joint allocation of computation and communication resources in multiuser mobile cloud computing. In: IEEE 14th Workshop on Signal Processing Advances in Wireless Communications. SPAWC 2013, 26–30 (2013)

17.

Miao, D., Zhu, W., Luo, C., et al.: Resource allocation for cloud-based free viewpoint video rendering for mobile phones. In: 19th ACM International Conference on Multimedia ACM Multimedia, MM’11, pp. 1237–1240 (2011)

18.

Choi, S.K., Chung, K.S., Yu, H.: Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Clust. Comput. 17(3), 911–926 (2014)CrossRef

19.

Al-Sharif, Z.A., Jararweh, Y., Al-Dahoud, A., et al.: ACCRS: autonomic based cloud computing resource scaling. Clust. Comput. 20(3), 2479–2488 (2017)

20.

Wang, K., Yang, K., Magurawalage, C.: Joint energy minimization and resource allocation in C-RAN with mobile cloud. IEEE Trans. Cloud Comput. 99, 1 (2016)

21.

Chakroun, O., Cherkaoui, S.: Resource allocation for delay sensitive applications in mobile cloud computing. In: 41st IEEE Conference on Local Computer Networks, LCN, pp. 615–618 (2016)

22.

Raei, H., Yazdani, N.: Analytical performance models for resource allocation schemes of cloudlet in mobile cloud computing. J. Supercomput. 73(3), 1274–1305 (2017)CrossRef

23.

Mohammed, M.H., Baothman, F.: Intelligent workload management of computing resource allocation for mobile cloud computing. Int. J. Comput. Org. Trends 19(1), 8–19 (2015)

24.

Pompili, D., Hajisami, A., Viswanathan, H.: Dynamic provisioning and allocation in Cloud Radio Access Networks (C-RANs). Ad Hoc Netw. 30, 128–143 (2015)CrossRef

25.

Bohez, S., Verbelen, T., Simoens, P., et al.: Allocation algorithms for autonomous management of collaborative cloudlets. In: 2014 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 1–9 (2014)

26.

Deng, R., Lu, R., Lai, C., et al.: Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)

27.

Chang, Z., Gong, J., Zhou, Z., et al.: Resource allocation and data offloading for energy efficiency in wireless power transfer enabled collaborative mobile clouds. In: 2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS, pp. 336–341 (2015)

28.

Aral, A.: Modeling and optimization of resource allocation in distributed clouds. In: 2016 IEEE International Conference on Cloud Engineering Workshops, IC2EW, pp. 210–212 (2016)

29.

Meng, S., Wang, Y., Miao, Z., et al.: Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment. Peer-to-Peer Netw. Appl. (2017). doi:10.1007/s12083-017-0544-x

30.

Nishio, T., Shinkuma, R., Takahashi, T., et al.: Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud. In: 2013 1st International Workshop on Mobile Cloud Computing and Networking, MobileCloud, pp. 19–26 (2013)

31.

Chunlin, Li, Xin, Y., LaYuan, Li: Flexible service provisioning based on context constraint for enhancing user experience in service oriented mobile cloud. J. Netw. Comput. Appl. 66, 250–261 (2016)CrossRef

32.

Chunlin, L., Layuan, L.: Cost and energy aware service provisioning for mobile client in cloud computing environment. J. Supercomput. 71(4), 1196–1223 (2015)CrossRef

33.

Chunlin, L., Layuan, L.: Exploiting composition of mobile devices for maximizing user QoS under energy constraints in mobile grid. Inf. Sci. 279(20), 654–670 (2014)CrossRef

34.

Kelly, F., Maulloo, A., Tan, D.: Rate control for communication networks: shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49(3), 237–252 (1998)CrossRefMATH

35.

Everett, H.: Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Oper. Res. 11(3), 399–417 (1963)MathSciNetCrossRefMATH

36.

Luh, P.B., Hoitomt, D.J.: Scheduling of manufacturing systems using the Lagrangian relaxation technique. IEEE Trans. Autom. Control 38(7), 1066–1079 (1993)MathSciNetCrossRef

37.

Kuhn, H.W., Tucker, A.W.: “Nonlinear programming”. In: Proceedings of 2nd Berkeley Symposium. University of California Press, Berkeley, pp. 481–492 (1951)

38.

Amazon EC2 instances, http://aws.amazon.com/ec2/instance-types/

39.

Youku. Available on http://www.youku.com/, September 2016

Titel: Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud
verfasst von: Li Chunlin
Tang Jianhang
Luo Youlong
Publikationsdatum: 19.09.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2018
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1171-2

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+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2018

CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing

A novel Bit Vector Product algorithm for mining frequent itemsets from large datasets using MapReduce framework

An empirical evaluation of energy-aware load balancing technique for cloud data center

Parallel multilayer particle collision detection method based on performance estimation

An accurate resource scheduling system for virtual machines based on CPU load monitoring and assessment

Performance prediction of parallel computing models to analyze cloud-based big data applications