Scheduling is a core component within distributed systems to determine optimal allocation of tasks within servers. This is challenging within modern Cloud computing systems - comprising millions of tasks executing in thousands of heterogeneous servers. Theoretical scheduling is capable of providing complete and sophisticated algorithms towards a single objective function. However, Cloud computing systems pursue multiple and oftentimes conflicting objectives towards provisioning high levels of performance, availability, reliability and energy-efficiency. As a result, theoretical scheduling for Cloud computing is performed by simplifying assumptions for applicability. This is especially true for task utilization patterns, which fluctuate in practice yet are modelled as piecewise constant in theoretical scheduling models. While there exists work for modelling dynamic Cloud task patterns for evaluating applied scheduling, such models are incompatible with the inputs needed for theoretical scheduling - which require such patterns to be represented as boxes. Presently there exist no methods capable of accurately converting real task patterns derived from empirical data into boxes. This results in a significant gap towards theoreticians understanding and proposing algorithms derived from realistic assumptions towards enhanced Cloud scheduling. This work proposes resource boxing - an approach for automated conversion of realistic task patterns in Cloud computing directly into box-inputs for theoretical scheduling. We propose four resource conversion algorithms capable of accurately representing real task utilization patterns in the form of scheduling boxes. Algorithms were evaluated using production Cloud trace data, demonstrating a difference between real utilization and scheduling boxes less than 5%. We also provide an application for how resource boxing can be exploited to directly translate research from the applied community into the theoretical community
