Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Network-Level Support for Big Data Computing

  • Fábio Diniz Rossi
  • Guilherme da Cunha RodriguesEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_45

Introduction

Currently, a huge amount of data has been created from several distributed sources. In this scenario, a new problem has emerged: how to develop and deploy infrastructures (i.e., storage, network, processing) that are scalable and elastic enough to handle this massive amount of data in a suitable way. The big data concept is related to the capacity of such infrastructures to cope with this enormous amount of data along with quality of service (QoS) metrics that include performance, timeliness, and availability (Assunção et al. 2015).

Scalability is the capacity to enhance the infrastructure by increasing the number of computational resources at the same pace that the quantity of data to be processed grows. It means that the infrastructure must be flexible enough to grow based on the demand for computational resources in order to provide high-quality services (Rodrigues et al. 2016).

To deliver big data demands, the whole infrastructure must be elastic as well. In other...

This is a preview of subscription content, log in to check access.

References

  1. Al-Fares M, Loukissas A, Vahdat A (2008) A scalable commodity data center network architecture. ACM SIGCOMM – Comput Commun Rev 38-4(October 2008):63–74Google Scholar
  2. Assunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R (2015) Big data computing and clouds: trends and future directions. J Parallel Distrib Comput 79–80:3–15CrossRefGoogle Scholar
  3. Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. SIGOPS Oper Syst Rev 37(5):164–177CrossRefGoogle Scholar
  4. Bari F, Boutaba R, Esteves R (2013) Data center network virtualization: a survey. IEEE Commun Surv Tutorials 15-2:909–928CrossRefGoogle Scholar
  5. Barona R, Anita EAM (2017) A survey on data breach challenges in cloud computing security: issues and threats, 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, pp 1–8Google Scholar
  6. Berggreen F, Litjens R (2006) Performance analysis of access selection and transmit diversity in multi-access networks. Proceedings of the 12th annual international conference on mobile computing and networking, Los Angeles, 23–29 Sept 2006, vol 1, pp 251–261Google Scholar
  7. Borovick L, Villars RL (2012) The critical role of the network in big data applications, White paper. IDC/CISCO, FraminghamGoogle Scholar
  8. da Cunha Rodrigues G, Calheiros RN, Guimarães VT, dos Santos GL, de Carvalho MB, Granville LZ, Tarouco L, Buyya R (2016) Monitoring of cloud computing environments: concepts, solutions, trends and future directions. Proceedings of the 31st annual ACM symposium on applied computing, Pisa, 4–8 Apr 2016, vol 1–2, pp 378–383Google Scholar
  9. DelValle R, Rattihalli G, Beltre A, Govin-Daraju M, Lewis M (2016) Exploring the design space for optimizations with Apache Aurora and Mesos, IEEE international conference on Cloud Computing (CLOUD) applications track, 2016Google Scholar
  10. Dirk M (2014) Docker: lightweight Linux containers for consistent development and deployment. Linux J 2014(239):2Google Scholar
  11. Dlamini NN, Johnston K (2016) The use, benefits and challenges of using the Internet of Things (IoT) in retail businesses: a literature review, 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE), Durban, pp 430–436Google Scholar
  12. Gao G, Xiao M, Wu J, Han K, Huang L, Zhao Z (2017) Opportunistic mobile data offloading with deadline constraints. IEEE Trans Parallel Distrib Syst PP(99):1–1Google Scholar
  13. Garrett T, Dustdar S, Bona LCE, Duarte EP (2017) Ensuring network neutrality for future distributed systems, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, pp 1780–1786Google Scholar
  14. Guo Z, Yang Y (2015) On nonblocking multirate multicast fat-tree data center networks with server redundancy. IEEE Trans Comput 64:1058–1073MathSciNetCrossRefGoogle Scholar
  15. Klas G (2017) Edge computing and the role of cellular networks. Computer 50(10):40–49CrossRefGoogle Scholar
  16. Lemeshko AV, Vavenko TV (2017) Development and research of the flow model of adaptive routing in the software-defined networks with load balancing. Proc Tomsk State Univ Control Syst Radioelectron 29(3):100–108Google Scholar
  17. Lomotey RK, Deters R(2014) Analytics-as-a-Service (AaaS) tool for unstructured data mining, 2014 IEEE international conference on cloud engineering, Boston, pp 319–324Google Scholar
  18. Mallika C, Selvamuthukumaran S (2017) Hadoop framework: analyzes workload prediction of data from cloud computing, 2017 international conference on IoT and application (ICIOT). Nagapattinam, India, pp 1–6Google Scholar
  19. Mary BF, Amalarethinam DIG (2017) Data security enhancement in public cloud storage using data obfuscation and steganography, 2017 World Congress on Computing and Communication Technologies (WCCCT), Tiruchirappalli, Tamil Nadu, pp 181–184Google Scholar
  20. Mijumbi R, Serrat J, Gorricho JL, Bouten N, De Turck F, Boutaba R (2016) Network function virtualization: state-of-the-art and research challenges. IEEE Commun Surv Tutorials 18(1):236–262CrossRefGoogle Scholar
  21. Seifert R (2000) The switch book: the complete guide to LAN switching technology, 1st edn. Wiley, New YorkGoogle Scholar
  22. Subramoni H, Lu X, Panda DK (2017) A scalable network-based performance analysis tool for MPI on large-scale HPC systems, 2017 IEEE International Conference on Cluster Computing (CLUSTER), Honolulu, pp 354–358Google Scholar
  23. Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, Saha B, Curino C, O’Malley O, Radia S, Reed B, Baldeschwieler E (2013) Apache Hadoop YARN: yet another resource negotiator, Proceedings of the third ACM Symposium on Cloud Computing (SOCC)Google Scholar
  24. Xavier MG, Neves MV, Rossi FD, Ferreto TC, Lange T, De Rose CAF (2013) Performance evaluation of container-based virtualization for high performance computing environments, 2013 21st Euromicro international conference on parallel, distributed, and network-based processing, Belfast, pp 233–240Google Scholar
  25. Xavier B, Ferreto T, Jersak L (2016) Time provisioning evaluation of KVM, Docker and Unikernels in a cloud platform, 2016 16th IEEE/ACM international symposium on Cluster, Cloud and Grid Computing (CCGrid), Cartagena, pp 277–280Google Scholar
  26. Yousefpour A, Ishigaki G, Jue JP (2017) Fog computing: towards minimizing delay in the internet of things, 2017 IEEE International Conference on Edge Computing (EDGE), Honolulu, pp 17–24Google Scholar
  27. Zhao Y, Zhang P, Wang Y, Jin Y (2017) SDN-enabled rule verification on data plane. IEEE Commun Lett PP(99):1–1Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Fábio Diniz Rossi
    • 1
  • Guilherme da Cunha Rodrigues
    • 2
    Email author
  1. 1.Federal Institute of Education Science, and Technology Farroupilha (IFFAR)AlegreteBrazil
  2. 2.Federal Institute of Education Science and Technology Sul-Rio Grandense (IFSUL)CharqueadasBrazil

Section editors and affiliations

  • Rodrigo N. Calheiros
    • 1
  • Marcos Dias de Assuncao
    • 2
  1. 1.School of Computing, Engineering and MathematicsWestern Sydney UniversityPenrithAustralia
  2. 2.Inria, LIP, ENS LyonLyonFrance