PrEstoCloud: Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing

Yiannis Verginadis, Iyad Alshabani, Gregoris Mentzas, Nenad Stojanovic

2017

Abstract

Among the greatest challenges of cloud computing is to automatically and efficiently exploit infrastructural resources in a way that minimises cloud fees without compromising the performance of resource demanding cloud applications. In this aspect the consideration of using processing nodes at the edge of the network, increases considerably the complexity of these challenges. PrEstoCloud idea encapsulates a dynamic, distributed, self-adaptive and proactively configurable architecture for processing Big Data streams. In particular, PrEstoCloud aims to combine real-time Big Data, mobile processing and cloud computing research in a unique way that entails proactiveness of cloud resources use and extension of the fog computing paradigm to the extreme edge of the network. The envisioned PrEstoCloud solution is driven by the microservices paradigm and has been structured across five different conceptual layers: i) Meta-management; ii) Control; iii) Cloud infrastructure; iv) Cloud/Edge communication and v) Devices, layers.

References

  1. Cisco (2015) White Paper: Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are. Available online at: http://www.cisco.com/c/dam/ en_us/solutions/trends/iot/docs/computingoverview.pdf.
  2. Cuomo, G., Martin, B.,K., Smith, K.,B., Ims, S., Rehn, H., Haberkorn, M., Parikh, J. (2013). Developing Edge Computing Applications. IBM White Paper, Available online at: http://www.ibm.com/developerworks/cn/ websphere/download/pdf/OnDemandEdgeComputing. pdf.
  3. Darpa, (1981). Transmission Control Protocol. Darpa Internet Program Protocol Specification. Available online at: https://www.ietf.org/rfc/rfc793.txt.
  4. Gartner (2016), Innovation Insight for Dynamic Optimization Technology for Infrastructure Resources and Cloud Services. Available online at: https:// www.gartner.com/doc/3231420?srcId=1- 2819006590&cm_sp=gi-_-rr-_-top.
  5. Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S., S., 2015. Lambda architecture for cost-effective batch and speed big data processing. In IEEE International Conference on Big Data (Big Data), DOI: 10.1109/BigData.2015.7364082.
  6. Kokkinos, P., Varvarigou, T.A., Kretsis, A., Soumplis, P., Varvarigos E.A., 2015. SuMo: Analysis and Optimization of Amazon EC2 Instances. J Grid Computing 13(2): 255-274. doi:10.1007/s10723-014- 9311-x.
  7. Mims, M., (2014). Forget 'the Cloud'; 'the Fog' Is Tech's Future. In The Wall Street Journal. Available online at: http://www.wsj.com/articles/SB100014240527023049 08304579566662320279406.
  8. Mone, G., 2013. Beyond Hadoop. In Communications of the ACM, Vol. 56 (1), pp. 22-24.
  9. Rossi F., P. van Beek, and T. Walsh, editors. Handbook of Constraint Programming, volume 2 of Foundations of Artificial Intelligence. Elsevier, 2006.
  10. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., 2016. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), pp 637 - 646.
  11. Wolski, R., Brevik, J., 2014. Using Parametric Models to Represent Private Cloud Workloads. IEEE Trans. Services Computing 7(4): 714-725.
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Paper Citation


in Harvard Style

Verginadis Y., Alshabani I., Mentzas G. and Stojanovic N. (2017). PrEstoCloud: Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing . In Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-243-1, pages 611-617. DOI: 10.5220/0006359106110617


in Bibtex Style

@conference{closer17,
author={Yiannis Verginadis and Iyad Alshabani and Gregoris Mentzas and Nenad Stojanovic},
title={PrEstoCloud: Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing},
booktitle={Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2017},
pages={611-617},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006359106110617},
isbn={978-989-758-243-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - PrEstoCloud: Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing
SN - 978-989-758-243-1
AU - Verginadis Y.
AU - Alshabani I.
AU - Mentzas G.
AU - Stojanovic N.
PY - 2017
SP - 611
EP - 617
DO - 10.5220/0006359106110617