Losing control:the case for emergent software systems using autonomous assembly, perception and learning

Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software which places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to explore alternative system assemblies and locate optimal solutions. Based on our experience to date, we define the problem space of emergent software, and we present a working case study of an emergent web server. Our results demonstrate two aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions; and that these assemblies can be autonomously learned from generalised perception data while the system is online

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Julia Programming Language Benchmark Using a Flight Simulation

Julias goal to provide scripting language ease-of-coding with compiled language speed is explored. The runtime speed of the relatively new Julia programming language is assessed against other commonly used languages including Python, Java, and C++. An industry-standard missile and rocket simulation, coded in multiple languages, was used as a test bench for runtime speed. All language versions of the simulation, including Julia, were coded to a highly-developed object-oriented simulation architecture tailored specifically for time-domain flight simulation. A speed-of-coding second-dimension is plotted against runtime for each language to portray a space that characterizes Julias scripting language efficiencies in the context of the other languages. With caveats, Julia runtime speed was found to be in the class of compiled or semi-compiled languages. However, some factors that affect runtime speed at the cost of ease-of-coding are shown. Julias built-in functionality for multi-core processing is briefly examined as a means for obtaining even faster runtime speed. The major contribution of this research to the extensive language benchmarking body-of-work is comparing Julia to other mainstream languages using a complex flight simulation as opposed to benchmarking with single algorithms

Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

REX:a development platform and online learning approach for Runtime emergent software systems

Conventional approaches to self-adaptive software architectures require human experts to specify models, policies and processes by which software can adapt to its environment. We present REX, a complete platform and online learning approach for runtime emergent software systems, in which all decisions about the assembly and adaptation of software are machine-derived. REX is built with three major, integrated layers: (i) a novel component-based programming language called Dana, enabling discovered assembly of systems and very low cost adaptation of those systems for dynamic re-assembly; (ii) a perception, assembly and learning framework (PAL) built on Dana, which abstracts emergent software into configurations and perception streams; and (iii) an online learning implementation based on a linear bandit model, which helps solve the search space explosion problem inherent in runtime emergent software. Using an emergent web server as a case study, we show how software can be autonomously self-assembled from discovered parts, and continually optimized over time (by using alternative parts) as it is subjected to different deployment conditions. Our system begins with no knowledge that it is specifically assembling a web server, nor with knowledge of the deployment conditions that may occur at runtime