Run-Time Management (RTM) systems are used in embedded systems to dynamically adapt hardware performance to minimise energy consumption. A significant challenge is that RTM software can require laborious manual adjustment across different hardware platforms due to the diversity of architecture characteristics. Model-driven development offers the potential to simplify the management of platform diversity by shifting the focus away from handwritten platform-specific code to platform-independent models from which platform-specific implementations are automatically generated. Furthermore, the use of formal verification provides the means to ensure that implementations are correct-by-construction. In this paper, we present a framework for automatic generation of RTM implementations from platformindependent formal models. The methodology in designing the RTM systems uses a high-level mathematical language, Event-B, which can describe systems at different abstraction levels. A code generation tool is used to translate platform-independent Event-B RTM models to platform-specific

Abstract-Modern mobile devices contain powerful MultiProcessor System-on-Chips (MPSoCs) that are performance throttled by Dynamic Power Management (DPM) runtime systems to extend battery lifetime. Applications on mobile devices commonly generate highly interactive workloads, dependent on interaction between the processor cores, peripherals, external resources and the user, such as touch input during web-browsing. Inevitably, a subset of interactive workloads are affected by delays caused by data unavailability, e.g. loss or delay of data packets during voice-over-IP. At the same time, the system is required to respond quickly upon data retrieval to ensure that the user Quality of Experience (QoE) metrics (frame-rate, latency, etc.) are not degraded. Traditionally, operating systems have mitigated this problem with periodic sampling or event-driven approaches. Through experimentation using a mobile MPSoC platform, however, we demonstrate that improving the tuning of DPM parameters for certain interactive user inputs can provide energy savings of up to 21% or QoE improvements of up to 36%, when compared with the traditional approach. To capture these improvements, we propose a dynamic modeling of user input and data resource access times (e.g. mobile network bandwidth and latency) for interactive workloads, which is based on workload profiling and which we refer to herein as inelasticity analysis. The proposed approach is implemented through online tuning of a DPM runtime in the Android operating system and is validated through a Monte Carlo simulation of interactive workloads. In comparison to the default DPM tuning, the proposed approach achieves energy savings of 13% or QoE improvement of 27% or a selectable trade-off, e.g. 9% energy savings and 15% QoE improvement.

Mobile devices are limited in mass and volume reducing the viability of active device cooling implementations, this requires the use of less effective passive techniques to maintain device skin temperature levels. Application performance demands on a modern mobile device are driven by sustained performance workloads, such as 3D games, Virtual and Augmented Reality. Mobile System-on-Chips have corresponding increases in performance through both architectural changes and frequency of operation increases; which has resulted in the peak power consumption exceeding the sustainable thermal envelope defined by device skin temperature requirements. Existing thermal throttling techniques mitigate this by capping the frequency of operation of the System-on-Chip. Through experimentation with a modern smartphone platform using sequences from realworld applications, we demonstrate in this paper that Frequency Capping can have a significant effect on the performance of interactive applications, increasing the number of frame rate defects by up to 146%. We propose Task Utilization Scaling, a new lever for thermal throttling, which scales performance for critical interactive periods by the same factor as noncritical periods. Experiments demonstrate that the proposed approach can result in a decrease in frame rate defects of up to 18% compared with Frequency Capping or a skin temperature reduction of up to 2°C.

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