  In today's era of Internet of Things (IoT), where massive amounts of data are
produced by IoT and other devices, edge computing has emerged as a prominent
paradigm for low-latency data processing. However, applications may have
diverse latency requirements: certain latency-sensitive processing operations
may need to be performed at the edge, while delay-tolerant operations can be
performed on the cloud, without occupying the potentially limited edge
computing resources. To achieve that, we envision an environment where
computing resources are distributed across edge and cloud offerings. In this
paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric
hybrid cloud-edge framework, aiming to maximize the on-time completion of
computational tasks offloaded by applications with diverse latency
requirements. The design of CLEDGE is motivated by the networking challenges
that mixed reality researchers face. Our evaluation demonstrates that CLEDGE
can complete on-time more than 90% of offloaded tasks with modest overheads.
