Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 3/2016

01.09.2016

An autonomic approach for resource provisioning of cloud services

verfasst von: Mostafa Ghobaei-Arani, Sam Jabbehdari, Mohammad Ali Pourmina

Erschienen in: Cluster Computing | Ausgabe 3/2016

Einloggen

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

search-config
loading …

Abstract

Recently, there has been a significant increase in the use of cloud-based services that are offered in software as a service (SaaS) models by SaaS providers, and irregular access of different users to these cloud services leads to fluctuations in the demand workload. It is difficult to determine the suitable amount of resources required to run cloud services in response to the varying workloads, and this may lead to undesirable states of over-provisioning and under-provisioning. In this paper, we address improvements to resource provisioning for cloud services by proposing an autonomic resource provisioning approach that is based on the concept of the control monitor-analyze-plan-execute (MAPE) loop, and we design a resource provisioning framework for cloud environments. The experimental results show that the proposed approach reduces the total cost by up to 35 %, the number of service level agreement (SLA) violations by up to 40 %, and increases the resource utilization by up to 25 % compared with the other approaches.
Nächster Artikel Architecture-aware configuration and scheduling of matrix multiplication on asymmetric multicore processors

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
Literatur
1.
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(5), 599–616 (2009)CrossRef
2.
Buyya, R., Vecchiola, C., Selvi, S.T.: Mastering Cloud Computing: Foundations and Applications Programming. Newnes, Boston (2013)
3.
Manvi, S.S., Shyam, G.K.: Resource management for infrastructure as a service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)CrossRef
4.
Mustafa, S., Nazir, B., Hayat, A., Madani, S.A.: Resource management in cloud computing: taxonomy, prospects, and challenges. Comput. Electr. Eng. 47, 186–203 (2015)CrossRef
5.
Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: state of the art and research challenges. Computer 10, 38–45 (2007)CrossRef
6.
Papazoglou, M.P., Van Den Heuvel, W.J.: Service oriented architectures: approaches, technologies and research issues. VLDB J. 16(3), 389–415 (2007)CrossRef
7.
Li, Z., Zhang, H., O’Brien, L., Cai, R., Flint, S.: On evaluating commercial cloud services: a systematic review. J. Syst. Softw. 86(8), 2371–2393 (2013)CrossRef
8.
Rodríguez, S., Tapia, D.I., Sanz, E., Zato, C., de la Prieta, F., Gil, O.: Cloud computing integrated into service-oriented multi-agent architecture. In: Balanced Automation Systems for Future Manufacturing Networks, pp. 251–259. Springer, Berlin (2010)
9.
Piprani, B., Sheppard, D., Barbir, A.: Comparative analysis of SOA and cloud computing architectures using fact based modeling. In: On the Move to Meaningful Internet Systems: OTM. Workshops, pp. 524–533. Springer, Berlin (2013)
10.
Ojala, A.: Discovering and creating business opportunities for cloud services. J. Syst. Softw. 113, 408–417 (2015)CrossRef
11.
Varia, J.: Architecting Applications for the Amazon Cloud. Principles and Paradigms. Wiley Press, New York, Cloud Computing (2011)
12.
Coutinho, E.F., de Carvalho Sousa, F.R., Rego, P.A.L., Gomes, D.G., de Souza, J.N.: Elasticity in cloud computing: a survey. Ann. Telecommun. 1–21 (2015)
13.
Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(3), 559–592 (2014)CrossRef
14.
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53(3), 50–58 (2010)CrossRef
15.
Jacob, B., Lanyon-Hogg, R., Nadgir, D.K., Yassin, A.F.: A Practical Guide to the IBM Autonomic Computing Toolkit. IBM Redbooks, New York (2004)
16.
Maurer, M., Breskovic, I., Emeakaroha, V.C., Brandic, I.: Revealing the MAPE loop for the autonomic management of cloud infrastructures. In: IEEE Symposium on Computers and Communications (ISCC), pp. 147–152 (2011)
17.
Weingärtner, R., Bräscher, G.B., Westphall, C.B.: Cloud resource management: a survey on forecasting and profiling models. J. Netw. Comput. Appl. 47, 99–106 (2015)CrossRef
18.
Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (2003)
19.
Huebscher, M.C., McCann, J.A.: A survey of autonomic computing-degrees, models, and applications. ACM Comput. Surv. 40(3), 7 (2008)CrossRef
20.
Maurer, M., Brandic, I., Sakellariou, R.: Adaptive resource configuration for cloud infrastructure management. Futur. Gener. Comput. Syst. 29(2), 472–487 (2013)CrossRef
21.
Maurer, M., Brandic, I., Emeakaroha, V.C., Dustdar, S.: Towards knowledge management in self-adaptable clouds. In: 6th World Congress on Services (SERVICES-1), pp. 527–534 (2010)
22.
Koehler, M.: An adaptive framework for utility-based optimization of scientific applications in the cloud. J. Cloud Comput. 3(1), 1–12 (2014)CrossRef
23.
Emeakaroha, V.C., Brandic, I., Maurer, M., Dustdar, S.: Cloud resource provisioning and SLA enforcement via LoM2HiS framework. Concurr. Comput. 25(9), 1462–1481 (2013)CrossRef
24.
25.
Ritter T., Mitschang B., Mega C.: Dynamic provisioning of system topologies in the cloud. In: Enterprise Interoperability V, pp. 391–401. Springer, London (2012)
26.
Pop, F., Potop-Butucaru, M.: ARMCO: advanced topics in resource management for ubiquitous cloud computing: an adaptive approach. Futur. Gener. Comput. Syst. 54, 79–81 (2016)CrossRef
27.
Casalicchio, E., Silvestri, L.: Mechanisms for SLA provisioning in cloud-based service providers. Comput. Netw. 57(3), 795–810 (2013)CrossRef
28.
Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., Althebyan, Q.: Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Cluster Comput. 18(2), 919–932 (2015)CrossRef
29.
Muppala, S., Chen, G., Zhou, X.: Multi-tier service differentiation by coordinated learning-based resource provisioning and admission control. J. Parallel Distrib. Comput. 74(4), 2351–2364 (2014)CrossRef
30.
Misra, S., Krishna, P.V., Kalaiselvan, K., Saritha, V., Obaidat, M.S.: Learning automata-based QoS framework for cloud IaaS. IEEE Trans. Netw. Serv. Manag. 11(1), 15–24 (2014)CrossRef
31.
Morshedlou, H., Meybodi, M.R.: Decreasing impact of SLA violations: a proactive resource allocation approach for cloud computing environments. IEEE Trans. Cloud Comput. 2(2), 156–167 (2014)CrossRef
32.
Qavami, H.R., Jamali, S., Akbari, M.K., Javadi, B.: Dynamic Resource Provisioning in Cloud Computing: A Heuristic Markovian Approach. In: Cloud Computing, pp. 102–111. Springer, New York (2014)
33.
Durán, F., Salaün, G.: Robust and reliable reconfiguration of cloud applications. J. Syst. Softw. 1–14 (2015)
34.
Roy, N., Dubey, A., Gokhale, A.: Efficient auto-scaling in the cloud using predictive models for workload forecasting. In: IEEE International Conference on Cloud Computing (CLOUD), pp. 500–507 (2011)
35.
Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Futur. Gener. Comput. Syst. 28(1), 155–162 (2012)CrossRef
36.
Bahrpeyma, F., Haghighi, H., Zakerolhosseini, A.: An adaptive RL based approach for dynamic resource provisioning in cloud virtualized data centers. Computing, pp. 1–26 (2015)
37.
Yang, J., Liu, C., Shang, Y., Cheng, B., Mao, Z., Liu, C., Niu, L., Chen, J.: A cost-aware auto-scaling approach using the workload prediction in service clouds. Inf. Syst. Front. 16(1), 7–18 (2014)CrossRef
38.
Li, C.: Optimal resource provisioning for cloud computing environment. J. Supercomput. 62(2), 989–1022 (2012)
39.
Wu, L., Garg, S.K., Versteeg, S., Buyya, R.: SLA-based resource provisioning for hosted software-as-a-service applications in cloud computing environments. IEEE Trans. Serv. Comput. 7(3), 465–485 (2014)CrossRef
40.
Chaisiri, S., Lee, B.S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5(2), 164–177 (2012)CrossRef
41.
Hassan, M.M., Hossain, M.S., Sarkar, A.J., Huh, E.N.: Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform. Inf. Syst. Front. 16(3), 523–542 (2014)CrossRef
42.
Hassan, M.M., Song, B., Sarkar, A.J., Huh, E.N.: Distributed resource allocation games in horizontal dynamic cloud federation platform. Int. Inf. Inst. (Tokyo) Inf. 15(2), 847–865 (2012)
43.
Hassan, M.M., 2014. Cost-effective resource provisioning for multimedia cloud-based e-health systems. Multimed. Tools Appl., pp. 1–17
44.
Dawoud, W., Takouna, I. Meinel, C.: Elastic VM for cloud resources provisioning optimization. In: Advances in Computing and Communications, pp. 431–445. Springer, Berlin (2011)
45.
Han, R., Ghanem, M.M., Guo, L., Guo, Y., Osmond, M.: Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Futur. Gener. Comput. Syst. 32, 82–98 (2014)CrossRef
46.
Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Futur. Gener. Comput. Syst. 27(5), 871–879 (2011)CrossRef
47.
Narendra, K.S., Thathachar, M.A.: Learning automata: an introduction. Courier Corporation, North Chelmsford (2012)MATH
48.
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Practice Exp. 41(1), 23–50 (2011)CrossRef
49.
Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: A performance analysis of EC2 cloud computing services for scientific computing. In: Cloud Computing, pp. 115–131. Springer, Berlin (2010)
Metadaten
Titel
An autonomic approach for resource provisioning of cloud services
verfasst von
Mostafa Ghobaei-Arani
Sam Jabbehdari
Mohammad Ali Pourmina
Publikationsdatum
01.09.2016
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 3/2016
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-016-0574-9

Weitere Artikel der Ausgabe 3/2016

Cluster Computing 3/2016 Zur Ausgabe

OriginalPaper

A multi-level text representation model within background knowledge based on human cognitive process for big data analysis

OriginalPaper

Vocabulary optimization process using similar phoneme recognition and feature extraction

OriginalPaper

Weighted natural neighborhood graph: an adaptive structure for clustering and outlier detection with no neighborhood parameter

OriginalPaper

An efficient multi-task PaaS cloud infrastructure based on docker and AWS ECS for application deployment

OriginalPaper

Performance analysis of optical interconnects’ architectures for data center networks

OriginalPaper

QDR: a QoS-aware data replication algorithm for Data Grids considering security factors