Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.

Impact of single parameter changes on Ceph cloud storage performance

In a general purpose cloud system efficiencies are yet to be had from supporting diverse applications and their requirements within a storage system used for a private cloud. Supporting such diverse requirements poses a significant challenge in a storage system that supports fine grained configuration on a variety of parameters. This paper uses the Ceph distributed file system, and in particular its global parameters, to show how a single changed parameter can effect the performance for a range of access patterns when tested with an OpenStack cloud system.

The framework for REVIEWS: an exploration into design principles for an electronic medical early warning system observation chart

The observation chart is for many health professionals (HPs) the primary source of objective information relating to the health of a patient. Information Systems (IS) research has demonstrated the positive impact of good interface design on decision making and it is logical that good observation chart design can positively impact healthcare decision making. Despite the potential for good observation chart design, there is a paucity of observation chart design literature, with the primary source of literature leveraging Human Computer Interaction (HCI) literature to design better charts. While this approach has been successful, this design approach introduces a gap between understanding of the tasks performed by HPs when using charts and the design features implemented in the chart. Good IS allow for the collection and manipulation of data so that it can be presented in a timely manner that support specific tasks. Good interface design should therefore consider the specific tasks being performed prior to designing the interface. This research adopts a Design Science Research (DSR) approach to formalise a framework of design principles that incorporates knowledge of the tasks performed by HPs when using observation charts and knowledge pertaining to visual representations of data and semiology of graphics. This research is presented in three phases, the initial two phases seek to discover and formalise design knowledge embedded in two situated observation charts: the paper-based NEWS chart developed by the Health Service Executive in Ireland and the electronically generated eNEWS chart developed by the Health Information Systems Research Centre in University College Cork. A comparative evaluation of each chart is also presented in the respective phases. Throughout each of these phases, tentative versions of a design framework for electronic vital sign observation charts are presented, with each subsequent iteration of the framework (versions Alpha, Beta, V0.1 and V1.0) representing a refinement of the design knowledge. The design framework will be named the framework for the Retrospective Evaluation of Vital Sign Information from Early Warning Systems (REVIEWS). Phase 3 of the research presents the deductive process for designing and implementing V0.1 of the framework, with evaluation of the instantiation allowing for the final iteration V1.0 of the framework. This study makes a number of contributions to academic research. First the research demonstrates that the cognitive tasks performed by nurses during clinical reasoning can be supported through good observation chart design. Secondly the research establishes the utility of electronic vital sign observation charts in terms of supporting the cognitive tasks performed by nurses during clinical reasoning. Third the framework for REVIEWS represents a comprehensive set of design principles which if applied to chart design will improve the usefulness of the chart in terms of supporting clinical reasoning. Fourth the electronic observation chart that emerges from this research is demonstrated to be significantly more useful than previously designed charts and represents a significant contribution to practice. Finally the research presents a research design that employs a combination of inductive and deductive design activities to iterate on the design of situated artefacts.