Energy consumption is a growing concern in several fields, from mobile devices to large data centers. Developers need detailed data on the energy consumption of their software to mitigate consumption issues. Previous approaches have a broader focus, such as on specific functions or programs, rather than source code statements. They primarily focus on estimating the CPU's energy consumption using point estimates, thereby disregarding other hardware effects and limiting their use for statistical reasoning and explainability. We developed a novel methodology to address the limitations of measuring only the CPU's consumption and using point estimates, focusing on predicting the energy usage of statically typed JVM-based programming languages, such as Java and Scala. We measure the energy consumption of Bytecode patterns, the translation from the programming language's source code statement to their Java Bytecode representation. With the energy measurements, we construct a statistical model using Bayesian statistics, which allows us to predict the energy consumption through statistical distributions and analyze individual factors. The model includes three factors we obtain statically from the code: data size, data type, operation, and one factor about the hardware platform the code executes on: device. To validate our methodology, we implemented it for Java and evaluated its energy predictions on unseen programs. We observe that all four factors are influential, notably that two devices of the same model may differ in energy consumption and that the operations and data types cause consumption differences. The experiments also show that the energy prediction of programs closely follows the program's real energy consumption, validating our approach. Our work presents a methodology for constructing an energy model that future work, such as verification tools, can use for their energy estimates.

Deep Learning-based (DL) applications are becoming increasingly popular and advancing at an unprecedented pace. While many research works are being undertaken to enhance Deep Neural Networks (DNN) -- the centerpiece of DL applications -- practical deployment challenges of these applications in the Cloud and Edge systems, and their impact on the usability of the applications have not been sufficiently investigated. In particular, the impact of deploying different virtualization platforms, offered by the Cloud and Edge, on the usability of DL applications (in terms of the End-to-End (E2E) inference time) has remained an open question. Importantly, resource elasticity (by means of scale-up), CPU pinning, and processor type (CPU vs GPU) configurations have shown to be influential on the virtualization overhead. Accordingly, the goal of this research is to study the impact of these potentially decisive deployment options on the E2E performance, thus, usability of the DL applications. To that end, we measure the impact of four popular execution platforms (namely, bare-metal, virtual machine (VM), container, and container in VM) on the E2E inference time of four types of DL applications, upon changing processor configuration (scale-up, CPU pinning) and processor types. This study reveals a set of interesting and sometimes counter-intuitive findings that can be used as best practices by Cloud solution architects to efficiently deploy DL applications in various systems. The notable finding is that the solution architects must be aware of the DL application characteristics, particularly, their pre- and post-processing requirements, to be able to optimally choose and configure an execution platform, determine the use of GPU, and decide the efficient scale-up range.