Research Article
DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-67537-0_23, author={Fuxiao Zhou and Haopeng Chen}, title={DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Collaborative storage Storage as a service Edge computing Small-scale Local area Resource management}, doi={10.1007/978-3-030-67537-0_23} }- Fuxiao Zhou
Haopeng Chen
Year: 2021
DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_23
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.
