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Deep Learning-based (DL) applications are becoming increasingly popular andadvancing at an unprecedented pace. While many research works are beingundertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DLapplications -- practical deployment challenges of these applications in theCloud and Edge systems, and their impact on the usability of the applicationshave not been sufficiently investigated. In particular, the impact of deployingdifferent virtualization platforms, offered by the Cloud and Edge, on theusability of DL applications (in terms of the End-to-End (E2E) inference time)has remained an open question. Importantly, resource elasticity (by means ofscale-up), CPU pinning, and processor type (CPU vs GPU) configurations haveshown to be influential on the virtualization overhead. Accordingly, the goalof this research is to study the impact of these potentially decisivedeployment options on the E2E performance, thus, usability of the DLapplications. To that end, we measure the impact of four popular executionplatforms (namely, bare-metal, virtual machine (VM), container, and containerin VM) on the E2E inference time of four types of DL applications, uponchanging processor configuration (scale-up, CPU pinning) and processor types.This study reveals a set of interesting and sometimes counter-intuitivefindings that can be used as best practices by Cloud solution architects toefficiently deploy DL applications in various systems. The notable finding isthat the solution architects must be aware of the DL applicationcharacteristics, particularly, their pre- and post-processing requirements, tobe able to optimally choose and configure an execution platform, determine theuse of GPU, and decide the efficient scale-up range.

Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications