RELAX: a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self-adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system.     

Architecture-based resilience evaluation for self-adaptive systems

One of the major challenges related to self-adaptive software systems is the provision of assurances that the system is resilient against changes that may occur either in the system or its environment. These assurances should be based on complementary sources of evidence that collectively justify that the system is able to attain the specified levels of resilience. The contribution of this paper is the definition and development of an architecture-based approach that evaluates by comparison the adaptation mechanisms of a self-adaptive software system. The proposed approach relies on the identification of representative environmental and system changeloads (i.e., sequences of changes) used in the run-time stimulation of the system. The system response obtained from this stimulation is collected and aggregated into a probabilistic model that is employed in the evaluation of system resilience. Our approach is intended to be used before deployment, since the process often involves putting the system through adverse conditions which are not adequate when the system is in production. The feasibility and effectiveness of the proposed approach is demonstrated in the context of Rainbow, an architecture-based platform for self- adaptation, and Znn.com, a case study that reproduces the typical infrastructure for a news website.     

Formal modeling of evolving self-adaptive systems

In this paper, we present a formal model, named PobSAM (Policy-based Self-Adaptive Model), for developing and modeling self-adaptive evolving systems. In this model, policies are used as a mechanism to direct and adapt the behavior of self-adaptive systems. A PobSAM model is a collection of autonomous managers and managed actors. The managed actors are dedicated to the functional behavior while the autonomous managers govern the behavior of managed actors by enforcing suitable policies. A manager has a set of configurations including two types of policies: governing policies and adaptation policies. To adapt the system behavior in response to the changes, the managers switch among different configurations. We employ the combination of an algebraic formalism and an actor-based model to specify this model formally. Managed actors are expressed by an actor model. Managers are modeled as meta-actors whose configurations are described using a multi-sorted algebra called CA. We provide an operational semantics for PobSAM using labeled transition systems. Furthermore, we provide behavioral equivalence of different sorts of CA in terms of splitting bisimulation and prioritized splitting bisimulation. Equivalent managers send the same set of messages to the actors. Using our behavioral equivalence theory, we can prove that the overall behavior of the system is preserved by substituting a manager by an equivalent one.     

Probabilistic approximation of runtime quantitative verification in self-adaptive systems

Cyber-physical systems (CPS) are expected to continuously monitor the physical components to autonomously calculate appropriate runtime reactions to deal with the uncertain environmental conditions. Self-adaptation, as a promising concept to fulfill a set of provable rules, majorly needs runtime quantitative verification (RQV). Taking a few probabilistic variables into account to represent the uncertainties, the system configuration will be extremely large. Thus, efficient approaches are needed to reduce the model state-space, preferably with certain bounds on the approximation error. In this paper, we propose an approximation framework to efficiently approximate the entire model of a self-adaptive system. We split up the large model into strongly-connected components (SCCs), apply the approximation algorithm separately on each SCC, and integrate the result of each part using a centralized algorithm. Due to a number of changes in probabilistic variables, it is not possible to use static models. Addressing this issue, we have deployed parametric Markov decision process. In order to apply approximation on the model, the notion of ε-approximate probabilistic bisimulation is utilized that introduces the approximation level ε. We show that our approximation framework offers a certain error bound on each level of approximation. Then, we denote that the approximation framework is appropriate to be applied in decision-making process of self-adaptive systems where the models are relatively large. The results reveal that we can achieve up to 50% size reduction in the approximate model while maintaining the accuracy about 95%. In addition, we discuss about the trade-off between efficiency and accuracy of our approximation framework.     

Toward a framework for self-adaptive workflows in cyber-physical systems

Software and Systems Modeling - With the establishment of Cyber-physical Systems (CPS) and the Internet of Things, the virtual world of software and services and the physical world of objects and...     

非确定型指派问题的求解算法

考虑了一类非确定型指派问题,每人所承担的工作数不确定,按每人至少承担一项工作,每项工作只允许一人承担的指派原则,针对人员无工作数限制和有工作数限制两种情况加以讨论和分析,借鉴Floyd算法的负回路思想,提出了一种迭代算法,并给出了应用此算法求解的具体实例。实验表明：与其他求解算法相比,该算法求解规模小,效率高,应用简便,易于编程实现。     

语音驱动自适应协同系统的决策方法

语音驱动的自适应协同系统应用日趋广泛,语音式任务目标具有的模糊性给自适应协同带来了挑战,自适应协同系统如何针对带有模糊性的语音进行任务决策是亟待解决的问题。为此,提出语音驱动的模糊自适应协同框架和决策方法。首先基于市场拍卖机制进行任务发布、投标和决策;然后把多属性的数据信息转化为二元语义并借助VIKOR方法进行排序;最后通过异构机器人自适应协同应用案例,说明了所提方法的决策步骤,表明了所提方法的有效性。     

基于自适应混合启发算法求解一类JSP问题

为解决一类Job Shop问题,提出了一种具有自适应机制的新的混合算法。该算法在分析和比较模拟退火算法和遗传算法的基础上,针对它们都缺乏全局指导机制的共同问题,引入具有自适应能力的全局指导策略,建立起个体与种群之间的反馈机制,混合后的算法还综合了两种启发算法的各自优点。通过具体的算例验证了该算法的有效性。     

On the JLQ problem with uncertainty

Trofino Neto et al. (1992) have shown that one can formulate an LQ problem in the presence of uncertainties in such a way that the state and input weighting matrices can be derived as the solution of a nonlinear optimization problem. Here it is shown that for linear systems with jumping parameters, the introduction of uncertainties in the state and input matrices can still be carried out and a control law derived as the solution of a similar, but more complex, problem. Moreover, both of the nonlinear optimization problems can be formulated as linear matrix inequality (LMI) problems, i.e., convex optimization problems. Hence, if an optimal solution exists, it can easily be obtained by means of some available LMI software packages. The existence of such a solution is proved under a restrictive assumption     

The inverse problem for compartmental systems

The current state of the inverse problem for compartmental systems is reviewed and analyzed in terms of three major phases, viz. model specification, identifiability and parameter estimation. Emphasis is given to a review of identifiability for linear compartmental systems and the need to put identifiability in perspective in the context of the inverse problem.     

Three self-adaptive multi-objective evolutionary algorithms for a triple-objective project scheduling problem

Finding a Pareto-optimal frontier is widely favorable among researchers to model existing conflict objectives in an optimization problem. Project scheduling is a well-known problem in which investigating a combination of goals eventuate in a more real situation. Although there are many different types of objectives based on the situation on hand, three basic objectives are the most common in the literature of the project scheduling problem. These objectives are: (i) the minimization of the makespan, (ii) the minimization of the total cost associated with the resources, and (iii) the minimization of the variability in resources usage. In this paper, three genetic-based algorithms are proposed for approximating the Pareto-optimal frontier in project scheduling problem where the above three objectives are simultaneously considered. For the above problem, three self-adaptive genetic algorithms, namely (i) A two-stage multi-population genetic algorithm (MPGA), (ii) a two-phase subpopulation genetic algorithm (TPSPGA), and (iii) a non-dominated ranked genetic algorithm (NRGA) are developed. The algorithms are tested using a set of instances built from benchmark instances existing in the literature. The performances of the algorithms are evaluated using five performance metrics proposed in the literature. Finally according to the technique for order preference by similarity to ideal solution (TOPSIS) the self-adaptive NRGA gained the highest preference rank, followed by the self-adaptive TPSPGA and MPGA, respectively.     

The quadratic eigenvalue problem in electric power systems

The application of the quadratic eigenvalue problem in electrical power systems is reviewed. The spectrum and pseudospectrum of an electrical power system are defined.     

自适应Jaya算法求解多目标柔性车间绿色调度问题

针对多目标柔性作业车间绿色调度问题(MO-FJGSP),建立优化目标为最大完工时间、机器总负荷和能耗最小的多目标数学模型,并设计一种基于Pareto最优解的自适应多目标Jaya算法(SAMO-Jaya)对该问题进行优化求解.算法采用两级实数编码方式实现工序排序与机器分配的编码表示,并设计一种转换机制实现将Jaya连续解...     

基于自适应遗传算法的Job Shop调度问题研究

求解Job Shop调度问题是个NP完全问题,为了提高遗传算法的性能,提出一种新的自适应遗传算法(NSGA)以解决Job Shop调度问题.采用活动调度解码方法、过滤个体适应度相同的筛选策略、改进自适应交叉变异概率等改进策略来提高算法性能,最后通过仿真比较分析证明该算法的先进性.     

A self-adaptive gradient projection algorithm for the nonadditive traffic equilibrium problem

Gradient projection (GP) algorithm has been shown as an efficient algorithm for solving the traditional traffic equilibrium problem with additive route costs. Recently, GP has been extended to solve the nonadditive traffic equilibrium problem (NaTEP), in which the cost incurred on each route is not just a simple sum of the link costs on that route. However, choosing an appropriate stepsize, which is not known a priori, is a critical issue in GP for solving the NaTEP. Inappropriate selection of the stepsize can significantly increase the computational burden, or even deteriorate the convergence. In this paper, a self-adaptive gradient projection (SAGP) algorithm is proposed. The self-adaptive scheme has the ability to automatically adjust the stepsize according to the information derived from previous iterations. Furthermore, the SAGP algorithm still retains the efficient flow update strategy that only requires a simple projection onto the nonnegative orthant. Numerical results are also provided to illustrate the efficiency and robustness of the proposed algorithm.     

Solution of the stochastic H ∞-optimization problem for discrete time linear systems under parametric uncertainty

The stochastic H _∞-optimization problem for a linear discrete time system with uncertain parameters is formulated and solved. The system operates in the presence of Gaussian random disturbances. The original problem with parametric uncertainty is reduced to the stochastic H _∞-optimization problem without uncertainty and having one extra input, which is essentially the mixed H ₂/H _∞-optimization problem. In a sense, the problem considered in this paper incorporates the classical H ₂/H _∞-and H _∞-optimization problems as limiting cases.     

A preliminary analysis of self-adaptive systems according to different issues

Software Quality Journal - Self-adaptive systems dynamically change their structure and behavior in response to changes in their execution environment to ensure the quality of the services they...