  Edge computing enables data processing and storage closer to where the data
are created. Given the largely distributed compute environment and the
significantly dispersed data distribution, there are increasing demands of data
sharing and collaborative processing on the edge. Since data shuffling can
dominate the overall execution time of collaborative processing jobs,
considering the limited power supply and bandwidth resource in edge
environments, it is crucial and valuable to reduce the communication overhead
across edge devices. Compared with data compression, compact data structures
(CDS) seem to be more suitable in this case, for the capability of allowing
data to be queried, navigated, and manipulated directly in a compact form.
However, the relevant work about applying CDS to edge computing generally
focuses on the intuitive benefit from reduced data size, while few discussions
about the challenges are given, not to mention empirical investigations into
real-world edge use cases. This research highlights the challenges,
opportunities, and potential scenarios of CDS implementation in edge computing.
Driven by the use case of shuffling-intensive data analytics, we proposed a
three-layer architecture for CDS-aided data processing and particularly studied
the feasibility and efficiency of the CDS layer. We expect this research to
foster conjoint research efforts on CDS-aided edge data analytics and to make
wider practical impacts.
