Serverless computing has allowed developers to write pieces of code comprising solely of the necessary functionality whilst not having to think about the underlying infrastructure. One prominent model is Function-as-a-Service (FaaS), where the code is structured into functions that run based on incoming events. This model was initially stateless, as new function calls can be instantiated at any location and there is no clear consensus on state whenever multiple instances are running simultaneously. Access to external persistent state is slow, making FaaS not suitable for low latency applications. Recent works have found different ways of incorporating state, resulting in Stateful FaaS (SFaaS). With the addition of state, these components are perfectly suited for distributed transactions. SFaaS frameworks try to outperform one another on metrics such as throughput and latency, but less work is performed on consistency.

In this thesis we look at the work on consistency of SFaaS transactions that has been done. We take Jepsen, a framework for testing transactions in distributed systems, and show that it can also be applied to SFaaS transactions. We then proceed to apply it to three SFaaS frameworks. We use Elle as a consistency checker to verify that the three frameworks comply with the consistency level they are advertised as. We have found that two of the tested frameworks do not have the consistency level promised. Facets of the SFaaS frameworks seem to have been overlooked, and diverging from the laid out benchmarking path quickly results in unintended behaviour.

Using transfer learning, convolutional neural networks for different purposes can have similar layers which can be reused by caching them, reducing their load time. Four ways of loading and executing these layers, bulk, linear, DeepEye and partial loading, were analysed under different memory constraints and different amounts of similar networks. When there is sufficient memory, caching will decrease the loading time and will always influence the single threaded bulk and linear mode. On the multithreaded approaches this only holds when the loading time is longer than the execution time. This depends largely on what network will be run. When memory constraints are applied caching can be a way to still run multiple networks without much increased cost. It can also be opted to use less memory on a device and use transfer learning with caching to still get the same results.