Abstract-This paper investigates a novel approach to derive self-adaptive software by automatically modifying the model of the application using a control-theoretical approach. Self adaptation is achieved at the model level to assure that the modelwhich lives alongside the application at run-time-continues to satisfy its reliability requirements, despite changes in the environment that might lead to a violation. We assume that the model is given in terms of a Discrete Time Markov Chain (DTMC). DTMCs can express reliability concerns by modeling possible failures through transitions to failure states. Reliability requirements may be expressed as reachability properties that constrain the probability to reach certain states, denoted as failure states.We assume that DTMCs describe possible variant behaviors of the adaptive system through transitions exiting a given state that represent alternative choices, made according to certain probabilities. Viewed from a control-theory standpoint, these probabilities correspond to the input variables of a controlled system-i.e., in the control theory lexicon, "control variables". Adopting the same lexicon, such variables are continuously modified at run-time by a feedback controller so as to ensure continuous satisfaction of the requirements despite disturbances, i.e., changes in the environment. Changes at the model level may then be automatically transferred to changes in the running implementation.The approach is methodologically described by providing a translation scheme from DTMCs to discrete-time dynamic systems, the formalism in which the controllers are derived. An initial empirical assessment is described for a case study. Conjectures for extensions to other models and other requirements concerns (e.g., performance) are discussed as future work.

A number of techniques have been proposed to provide runtime performance guarantees while minimizing power consumption. One drawback of existing approaches is that they work only on a fixed set of components (or actuators) that must be specified at design time. If new components become available, these management systems must be redesigned and reimplemented. In this paper, we propose PTRADE, a novel performance management framework that is general with respect to the components it manages. PTRADE can be deployed to work on a new system with different components without redesign and reimplementation. PTRADE's generality is demonstrated through the management of performance goals for a variety of benchmarks on two different Linux/x86 systems and a simulated 128-core system, each with different components governing power and performance tradeoffs. Our experimental results show that PTRADE provides generality while meeting performance goals with low error and close to optimal power consumption.

The paper discusses current approaches to the modelling and simulation of thermo-hydraulic processes, to be used as a tool for system studies in thermal power plant control. After reviewing the desirable features of simulation environments, an approach based on the Modelica language is presented and motivated. Finally, the general concepts presented above are exemplified by modelling a simple process based on a heat exchanger.

No abstract

No abstract

We introduce the hydrolysis stage and the ammonium dynamics into the AM2 model. ► A generic and systematic state-association approach is proposed. ► The new proposed model AM2HN is calibrated. ► The AM2HN model gives an accurate description of the dynamics of the ADM1 model. ► The AM2HN is robust with regard to moderate variations in the influent composition.

Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This article proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application. A variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks. Our results indicate that the most suitable decision mechanism can vary depending on the specific test case but adaptive and model predictive control systems tend to produce good performance and may work best in a priori unknown situations. A. 2012. Comparison of decision-making strategies for self-optimization in autonomic computing systems.

A formalism was recently introduced to instrument, monitor and control computer applications based on the rate of heartbeats they emit, thereby quantitatively signaling their progress toward goals. To date, the idea was however used essentially in an heuristic manner. This work first shows that a very simple dynamic heartbeat rate model can be devised, an that said model allows to address the corresponding control problems in a methodologically grounded way. A general solution is then devised, that can be realized through different actuation mechanisms, depending on which type of resource-CPU, memory, bandwidth, etc.-is constraining the application performance in the particular situation at hand. Experiments prove the efficacy of the proposed extension to the heartbeats framework, both with applications that fit the proposed model and with more complex test cases, for which said model is just a coarse approximation.