Intelligent Management of Mobile Systems through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads

Co-design and evaluation of a youth-informed organisational tool to enhance trauma-informed practices in the UK public sector: a study protoco

Introduction A trauma-informed approach (TIA) means working with awareness that people’s histories of trauma may shape the way they engage with services, organisations or institutions. Young people with adverse childhood experiences may be at risk of retraumatisation by organisational practices in schools and universities and by employers and health agencies when they seek support. There are limited evidence-based resources to help people working in the public sector to work with adolescents in trauma-informed ways and the needs of adolescents have not been central in resource development. This study contributes to public sector capacity to work in trauma-informed ways with adolescents by codesigning and evaluating the implementation of a youth-informed organisational resource. Methods and analysis This is an Accelerated Experience-based Co-design (AEBCD) Study followed by pre–post evaluation. Public sector organisations or services, and adolescents connected with them, will collaboratively reflect on lived experience data assembled through creative arts practice, alongside data from epidemiological national data sets. These will present knowledge about the impact of adverse childhood experiences on adolescents’ mental health (stage 1). Collaboratively, priorities (touch points) for organisational responses will be identified (stage 2), and a low-burden resource will be codesigned (stage 3) and offered for implementation (stage 4) and evaluation (stage 5) in diverse settings. The study will provide insights into what adolescents and public sector organisations in the UK want from a TIA resource, the experience of services/organisations in implementing this and recommendations for resource development and implementation. Ethics and dissemination The UK National Health Service Health Research Authority approved this study (23/WM/0105). Learning will be shared across study participants in a workshop at the end of the study. Knowledge products will include a website detailing the created resource and a youth-created film documenting the study process, the elements of the codesigned resource and experiences of implementation. Dissemination will target academic, healthcare, education, social care, third sector and local government settings via knowledge exchange events, social media, accessible briefings, conference presentations and publications

Practical and Robust Power Management for Wireless Sensor Networks

Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs

Adaptive Resource Management for Mobile Multiprocessors through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex.The complexity can be due to diverse workload characteristics with conflicting demands or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner.To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization).In this thesis, I explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system will reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads