Engineering emergence for cluster configuration

Distributed applications are being deployed on ever-increasing scale and with ever-increasing functionality. Due to the accompanying increase in behavioural complexity, self-management abilities, such as self-healing, have become core requirements. A key challenge is the smooth embedding of such functionality into our systems. Natural distributed systems such as ant colonies have evolved highly efficient behaviour. These emergent systems achieve high scalability through the use of low complexity communication strategies and are highly robust through large-scale replication of simple, anonymous entities. Ways to engineer this fundamentally non-deterministic behaviour for use in distributed applications are being explored. An emergent, dynamic, cluster management scheme, which forms part of a hierarchical resource management architecture, is presented. Natural biological systems, which embed self-healing behaviour at several levels, have influenced the architecture. The resulting system is a simple, lightweight and highly robust platform on which cluster-based autonomic applications can be deployed.

Towards policy-based self-configuration of embedded systems

This paper describes work towards the deployment of flexible self-management into real-time embedded systems. A challenging project which focuses specifically on the development of a dynamic, adaptive automotive middleware is described, and the specific self-management requirements of this project are discussed. These requirements have been identified through the refinement of a wide-ranging set of use cases requiring context-sensitive behaviours. A sample of these use-cases is presented to illustrate the extent of the demands for self-management. The strategy that has been adopted to achieve self-management, based on the use of policies is presented. The embedded and real-time nature of the target system brings the constraints that dynamic adaptation capabilities must not require changes to the run-time code (except during hot update of complete binary modules), adaptation decisions must have low latency, and because the target platforms are resource-constrained the self-management mechanism have low resource requirements (especially in terms of processing and memory). Policy-based computing is thus and ideal candidate for achieving the self-management because the policy itself is loaded at run-time and can be replaced or changed in the future in the same way that a data file is loaded. Policies represent a relatively low complexity and low risk means of achieving self-management, with low run-time costs. Policies can be stored internally in ROM (such as default policies) as well as externally to the system. The architecture of a designed-for-purpose powerful yet lightweight policy library is described. A suitable evaluation platform, supporting the whole life-cycle of feasibility analysis, concept evaluation, development, rigorous testing and behavioural validation has been devised and is described.

Flexible and robust run-time configuration for self-managing systems

This paper describes a methodology for deploying flexible dynamic configuration into embedded systems whilst preserving the reliability advantages of static systems. The methodology is based on the concept of decision points (DP) which are strategically placed to achieve fine-grained distribution of self-management logic to meet application-specific requirements. DP logic can be changed easily, and independently of the host component, enabling self-management behavior to be deferred beyond the point of system deployment. A transparent Dynamic Wrapper mechanism (DW) automatically detects and handles problems arising from the evaluation of self-management logic within each DP and ensures that the dynamic aspects of the system collapse down to statically defined default behavior to ensure safety and correctness despite failures. Dynamic context management contributes to flexibility, and removes the need for design-time binding of context providers and consumers, thus facilitating run-time composition and incremental component upgrade.

The influence of crank configuration on muscle activity and torque production during arm crank ergometry

This study investigated the effect of crank configuration on muscle activity and torque production during submaximal arm crank ergometry. Thirteen non-specifically trained male participants volunteered. During the research trials they completed a warm-up at 15 W before two 3-min exercise stages were completed at 50 and 100 W; subjects used either a synchronous or asynchronous pattern of cranking. During the final 30-s of each submaximal exercise stage electromyographic and torque production data were collected. After the data had been processed each parameter was analysed using separate 2-way ANOVA tests with repeated measures. The activity of all muscles increased in line with external workload, although a shift in the temporal pattern of muscle activity was noted between crank configurations. Patterns of torque production during asynchronous and synchronous cranking were distinct. Furthermore, peak, minimum and delta (peak-minimum) torque values were different (P < 0.05) between crank configurations at both workloads. For example, at 100 W, peak torque using synchronous [19.6 (4.3) Nm] cranking was higher (P < 0.05) compared to asynchronous [16.8 (1.6) Nm] cranking. In contrast minimum torque was lower (P < 0.05) at 100 W using synchronous [4.8 (1.7) Nm] compared to asynchronous [7.3 (1.2) Nm] cranking. There was a distinct bilateral asymmetry in torque production during asynchronous cranking with the dominant transmitting significantly more force to the crank arm. Taken together, these preliminary data demonstrate the complex nature of muscle activity during arm crank ergometry performed with an asynchronous or synchronous crank set-up. Further work is required to determine how muscle activity (EMG activity) and associated patterns of torque production influence physiological responses and functional capacity during arm crank ergometry.