Abstract
With the development of IoT and 5G, data are generated by the numerous smart end devices at each moment. Simultaneously, as the improvement of the hardware’s performance, computing and storage are partly transferred to the edge of the Internet. However, the core cloud and massive data centers are still responsible for management and coordination. In more and more local-area and small-scale scenarios such as a parking lot, an office building, or a college campus, these scenarios also need the edge nodes to offload computing and storage tasks. Moreover, in order to decrease costs and be lightweight, these scenarios need to decouple with the core cloud partly. In this paper, we proposed a collaborative edge-edge data storage service called DECS for edge computing in local-area scenarios. DECS can make the edge nodes collaborate with others. Such as trade-off to pick the most appropriate edge node to offload storage or computing tasks. DECS can also replicate data or generate forwarding rules in advance by predicting data’s popularity proactively.
In this paper, we evaluated DECS at two real scenarios compared with state-of-the-art research. The experiment results proved that DECS was more suitable for the local-area edge cluster. Which lowered the access latency, saved the total bandwidth, and improved the resource utilization of the whole edge cluster.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avasalcai, C., Tsigkanos, C., Dustdar, S.: Decentralized resource auctioning for latency-sensitive edge computing, pp. 72–76, July 2019. https://doi.org/10.1109/EDGE.2019.00027
Casadei, R., Viroli, M.: Coordinating computation at the edge: a decentralized, self-organizing, spatial approach, pp. 60–67, June 2019. https://doi.org/10.1109/FMEC.2019.8795355
D’Angelo, M.: Decentralized self-adaptive computing at the edge, pp. 144–148, May 2018
Decandia, G., et al.: Dynamo: Amazon’s highly available key-value store. 41(6), 205–220 (2007)
Freitag, F.: On the collaborative governance of decentralized edge microclouds with blockchain-based distributed ledgers, pp. 709–712, December 2018. https://doi.org/10.1109/WI.2018.000-7
Gabriel, T., Cornel-Cristian, A., Arhip-Calin, M., Zamfirescu, A.: Cloud storage. A comparison between centralized solutions versus decentralized cloud storage solutions using blockchain technology, pp. 1–5, September 2019. https://doi.org/10.1109/UPEC.2019.8893440
Gao, Q., Gao, L., Xue, T., Zhu, X., Zhao, X., Cao, R.: Multi-view learning based edge storage management strategy, pp. 366–371, August 2018. https://doi.org/10.1109/CBD.2018.00072
Ghemawat, S., Gobioff, H., Leung, S.T.: The Google file system. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles, Bolton Landing, NY, pp. 20–43 (2003)
Gheorghe, A., Crecana, C., Negru, C., Pop, F., Dobre, C.: Decentralized storage system for edge computing, pp. 41–49, June 2019. https://doi.org/10.1109/ISPDC.2019.00009
Khan, A., Muhammad, A., Kim, Y., Park, S., Tak, B.: EDGESTORE: a single namespace and resource-aware federation file system for edge servers, pp. 101–108, July 2018. https://doi.org/10.1109/EDGE.2018.00021
Li, X., Lian, Z., Qin, X., Abawajyz, J.: Delay-aware resource allocation for data analysis in cloud-edge system, pp. 816–823, December 2018. https://doi.org/10.1109/BDCloud.2018.00122
Li, Y., Luo, J., Jin, J., Xiong, R., Dong, F.: An effective model for edge-side collaborative storage in data-intensive edge computing, pp. 92–97, May 2018. https://doi.org/10.1109/CSCWD.2018.8465306
Lujic, I., Maio, V.D., Brandic, I.: Efficient edge storage management based on near real-time forecasts, pp. 21–30, May 2017. https://doi.org/10.1109/ICFEC.2017.9
Lv, H., Chen, D., Wang, Y.: Deployment of edge-computing in 5G NFV environment and future service-based architecture, pp. 811–816, December 2018. https://doi.org/10.1109/CompComm.2018.8780937
Monga, S.K., Ramachandra, S.K., Simmhan, Y.: ElfStore: a resilient data storage service for federated edge and fog resources, pp. 336–345 (2019)
Moon, J., Cho, S., Kum, S., Lee, S.: Cloud-edge collaboration framework for IoT data analytics, pp. 1414–1416, October 2018. https://doi.org/10.1109/ICTC.2018.8539664
Stanciu, A.: Blockchain based distributed control system for edge computing, pp. 667–671, May 2017. https://doi.org/10.1109/CSCS.2017.102
Xu, J., Chen, L., Zhou, P.: Joint service caching and task offloading for mobile edge computing in dense networks, pp. 207–215, April 2018. https://doi.org/10.1109/INFOCOM.2018.8485977
Yousefpour, A., et al.: All one needs to know about fog computing and related edge computing paradigms: a complete survey (2018)
Zheng, J., Dong, X., Zhang, T., Chen, J., Tong, W., Yang, X.: MicrothingsChain: edge computing and decentralized IoT architecture based on blockchain for cross-domain data sharing, pp. 350–355, October 2018. https://doi.org/10.1109/NANA.2018.8648780
Acknowledgment
This paper is supported by Project 213.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhou, F., Chen, H. (2021). DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-67537-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67536-3
Online ISBN: 978-3-030-67537-0
eBook Packages: Computer ScienceComputer Science (R0)