UC Santa Cruz

Emerging edge applications such as the Internet of Things (IoT) and Industry 4.0

require stringent real-time guarantees. This makes it infeasible to rely solely on faraway

cloud nodes for these applications. Similarly, edge machines are less capable and

reliable than cloud machines. To balance the trade-off of edge and cloud computing,

we propose a data infrastructure that spans both edge and cloud machines, that we call

edge-cloud computing. We tackle one of the fundamental data management challenges

in edge-cloud computing, the problem of data indexing. We propose Cooperative LSM

[3] (CooLSM), a distributed Log-Structured Merge Tree that is designed to overcome

the unique challenges of edge-cloud indexing such as machine and workload

heterogeneity and the communication latency asymmetry between the edge and the

cloud. To tackle these challenges, CooLSM deconstructs the structure of LSM into its

basic parts. This deconstruction allows a better distribution and placement of resources

across edge and cloud devices. For example, append-specific functionality is managed

at the edge to ensure appending and serving data in real-time, whereas resource-intensive

operations such as compaction are managed at the cloud where more

computing resources are available.