Towards autonomic application-sensitive partitioning for SAMR applications

Distributed structured adaptive mesh refinement (SAMR) techniques offer the potential for accurate and cost-effective solutions of physically realistic models of complex physical phenomena. However, the heterogeneous and dynamic nature of SAMR applications results in significant runtime management challenges. This paper investigates autonomic application-sensitive SAMR runtime management strategies and presents the design, implementation, and evaluation of ARMaDA, a self-adapting and optimizing partitioning framework for SAMR applications. ARMaDA monitors and characterizes application runtime state, and dynamically selects and invokes appropriate partitioning mechanisms that match current SAMR state and optimize its computational and communication performance. The advantages of the autonomic partitioning capabilities provided by ARMaDA are experimentally demonstrated.     

An overview of Dynamic Software Product Line architectures and techniques: Observations from research and industry

Over the last two decades, software product lines have been used successfully in industry for building families of systems of related products, maximizing reuse, and exploiting their variable and configurable options. In a changing world, modern software demands more and more adaptive features, many of them performed dynamically, and the requirements on the software architecture to support adaptation capabilities of systems are increasing in importance. Today, many embedded system families and application domains such as ecosystems, service-based applications, and self-adaptive systems demand runtime capabilities for flexible adaptation, reconfiguration, and post-deployment activities. However, as traditional software product line architectures fail to provide mechanisms for runtime adaptation and behavior of products, there is a shift toward designing more dynamic software architectures and building more adaptable software able to handle autonomous decision-making, according to varying conditions. Recent development approaches such as Dynamic Software Product Lines (DSPLs) attempt to face the challenges of the dynamic conditions of such systems but the state of these solution architectures is still immature. In order to provide a more comprehensive treatment of DSPL models and their solution architectures, in this research work we provide an overview of the state of the art and current techniques that, partially, attempt to face the many challenges of runtime variability mechanisms in the context of Dynamic Software Product Lines. We also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in DSPL models and software architectures.     

Dynamic adaptation of service compositions with variability models

Web services run in complex contexts where arising events may compromise the quality of the whole system. Thus, it is desirable to count on autonomic mechanisms to guide the self-adaptation of service compositions according to changes in the computing infrastructure. One way to achieve this goal is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone. In this paper, we propose a solution based on a semantically rich variability model to support the dynamic adaptation of service compositions. When a problematic event arises in the context, this model is leveraged for decision-making. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. These changes are reflected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which can be deployed at runtime. In order to reach optimum adaptations, the variability model and its possible configurations are verified at design time using Constraint Programming. An evaluation demonstrates several benefits of our approach, both at design time and at runtime.     

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

     

Towards Software Architecture and Mechanisms for Improving Runtime Variability of Internetware System

As an emerging software paradigm, Internetware is proposed to handle openness, dynamism of software systems in the context of Internet, which implies that such software systems typically have runtime variability that can be improved dynamically to handle various or even unexpected changes of requirements and open environment. Though many progresses of Internetware software technologies have been made to support the adaptation, evolution, context-awareness, etc. of Internetware, how to construct Internetware systems with the ability to improve their runtime variability is still a great challenge in the literature of software engineering. In this paper, we propose software architecture and mechanisms for Internetware systems to support the improvement of their runtime variability by combining software variability and autonomic computing techniques. The Internetware system is organized as three levels that are consist of variable autonomic elements and Internetware entities, and architecture of these software entities is defined and discussed respectively. Moreover, we put forward a series of runtime mechanisms based on these levels, including module selection, intermediator and horizontal management, to realize operations upon the variation points and variants in software architectures and thus achieve the improvement of runtime variability. We develop a sample of Personal Data Resource Network to depict the requirements and scenario of improving runtime variability, and further study the case based on our proposed approach to show its effectiveness and applicability.     

MaDe4IC: an abstract method for managing model dependencies in inter-organizational cooperations

Inter-organizational cooperations are complex in terms of coordination, agreements, and value creation for involved partners. When managing complex cooperations, it is vital to maintain models describing them. Changing one model to regain consistency with the running system might result in new inconsistencies. As a consequence, this maintenance phase grows in complexity with increasing number of models. In this context, challenges are to ensure consistency at design time and to monitor the system at runtime, i.e., at design time, consistency between different models describing the cooperation needs to be ensured. At runtime, behavior of the software system needs to be compared with its underlying models. In this paper, we propose a structured and model-independent method that supports ensuring and maintaining consistency between running system and underlying models for inter-organizational cooperations.     

Systematic literature review of the objectives,techniques, kinds,and architectures of models at runtime

In the context of software development, models provide an abstract representation of a software system or a part of it. In the software development process, they are primarily used for documentation and communication purposes in analysis, design, and implementation activities. Model-Driven Engineering (MDE) further increases the importance of models, as in MDE models are not only used for documentation and communication, but as central artefacts of the software development process. Various recent research approaches take the idea of using models as central artefacts one step further by using models at runtime to cope with dynamic aspects of ever-changing software and its environment. In this article, we analyze the usage of models at runtime in the existing research literature using the Systematic Literature Review (SLR) research method. The main goals of our SLR are building a common classification and surveying the existing approaches in terms of objectives, techniques, architectures, and kinds of models used in these approaches. The contribution of this article is to provide an overview and classification of current research approaches using models at runtime and to identify research areas not covered by models at runtime so far.     

Simplifying autonomic enterprise Java Bean applications via model-driven engineering and simulation

The goal of autonomic computing is to reduce the configuration, operational, and maintenance costs of distributed applications by enabling them to self-manage, self-heal, and self-optimize. This paper provides two contributions to the Model-Driven Engineering (MDE) of autonomic computing systems using Enterprise Java Beans (EJBs). First, we describe the structure and functionality of an MDE tool that visually captures the design of EJB applications, their quality of service (QoS) requirements, and the adaptations applied to their EJBs. Second, the paper describes how MDE tools can be used to generate code to simulate adaptive systems for verification and plug EJBs into a Java component framework that provides runtime adaptation capabilities.     

Agent能力承诺协作的自适应图规划协议生成算法

面向开放多智能体系统OMAS的自适应性体现在多Agent通过协作在运行时调整系统行为以适应环境的变化。针对现有多Agent设计时预定义协作机制无法满足运行时自适应协作的问题,提出了一种基于目标-能力-承诺GCC元模型生成自适应多Agent能力承诺协作图规划协议CCGP的算法。首先提出支持上下文环境语义相似度计算的GCC模型;然后将目标与能力（或者承诺）间的语义匹配度引入到基于能力协作的图规划协议中生成优化算法;最后以医疗垃圾AGV运输仿真系统为实验场景进行2组对比实验,实验结果表明CCGP算法对于生成图规划协议的执行时间有明显提高。     

Optimized temporal monitors for SystemC

SystemC is a modeling language built as an extension of C++. Its growing popularity and the increasing complexity of designs have motivated research efforts aimed at the verification of SystemC models using assertion-based verification (ABV), where the designer asserts properties that capture the design intent in a formal language such as PSL or SVA. The model then can be verified against the properties using runtime or formal verification techniques. In this paper we focus on automated generation of runtime monitors from temporal properties. Our focus is on minimizing runtime overhead, rather than monitor size or monitor-generation time. We identify four issues in monitor generation: state minimization, alphabet representation, alphabet minimization, and monitor encoding. We conduct extensive experimentation and identify a combination of settings that offers the best performance in terms of runtime overhead.     

On the Dynamic Semantics and the Timing Behavior of Ravenscar Kernels

Mature research advances in scheduling theory show that carefully-crafted concurrent computational models permit static analysis of real-time behavior. This evidence enables designers to consider using suitable forms of explicit concurrency to model the inherent concurrency of real-time systems. The Ravenscar Profile, a specifically tailored subset of the Ada 95 tasking model, defines a compact and efficient concurrent computational model, especially suited for the development of high integrity, high efficiency real-time systems.Ravenscar runtimes can be implemented by small, efficient, reliable and certifiable kernels. At least two such implementations already exist and are being industrially deployed. The simplicity and intrinsic determinism of Ravenscar kernels facilitate the definition of metrics that cater for very accurate characterization of the dynamic behavior of the runtime and of the execution time of its primitives. Accurate runtime metrics enable forms of response time analysis that minimize the pessimism in the prediction of the runtime influence on the application. This is especially useful for concurrent systems that exhibit significant dependency on runtime support services. This paper recalls the motivations of the Ravenscar Profile, outlines the definition of it and formulates a precise characterisation of the associated runtime metrics.     

Models@ run.time

Runtime adaptation mechanisms that leverage software models extend the applicability of model-driven engineering techniques to the runtime environment. Contemporary mission-critical software systems are often expected to safely adapt to changes in their execution environment. Given the critical roles these systems play, it is often inconvenient to take them offline to adapt their functionality. Consequently, these systems are required, when feasible, to adapt their behavior at runtime with little or no human intervention. A promising approach to managing complexity in runtime environments is to develop adaptation mechanisms that leverage software models, referred to as models@run. time. Work on models@run.time seeks to extend the applicability of models produced in model-driven engineering (MDE) approaches to the runtime environment. Models@run. time is a causally connected self-representation of the associated system that emphasizes the structure, behavior, or goals of the system from a problem space perspective.     

Towards modeling and runtime verification of self-organizing systems

According to the fact that the intrinsic dynamism of self-organizing systems challenges the existing methods of engineering for modeling reliable complex systems, in this paper, we propose a new formal-based method to model self-organizing systems. The capabilities of the proposed method which are used to address several challenges in design, development and analysis of self-organizing systems are: modularity and robustness, decentralized control and scalability, required adaptation types, flexible and adaptive control mechanism, separation of adaptation and business logic, and safe adaptation. To evaluate the proposed method, we use self-organizing traffic management system as a case study and exploit the proposed method for modeling this dynamic system. Moreover, we propose and employ a novel policy-based runtime verification mechanism to ensure that the safety properties are satisfied by the implementation at runtime. We provide our case study prototype using Java and the Ponder2 toolkit and apply our runtime verification method to show its proper reaction capabilities to the property violations. This benefit is the result of using dynamic policies in our method to control the behavior of systems.     

Adaptable,model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple “tenants” of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants—i.e. multi-tenancy—increases tenants’ concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants’ needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants’ security requirements. We use abstract models to capture service provider and multiple tenants’ security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.     

Task construction for model-based design of embedded control software

Constructing runtime tasks, or operating system-level processes/threads, from the components of software design models is crucial to the model-based development of embedded control software. A better method should explore more design choices and reduce the overheads of the runtime system to meet the timing and resource constraints of embedded control software. This paper presents a novel, two-step method for systematic and automatic construction of runtime tasks from software design models. It uses graph transformation to construct a task set meeting system-level end-to-end (e2e) timing constraints. Its first step decomposes the system-level e2e timing constraints into the components' timing constraints, which form a necessary condition for any valid and feasible schedule. The second step iteratively merges the components into tasks and sequences their executions. A thus-constructed task set is proven to meet both intercomponent precedence and system-level e2e timing constraints and to minimize runtime overheads by minimizing the total number of resultant tasks. Our evaluation results based on randomly generated software models have shown that the proposed method outperforms commonly used methods and is also scalable.     

Toward Open-World Software: Issue and Challenges

Traditional software development is based on the closed-world assumption that the boundary between system and environment is known and unchanging. However, this assumption no longer works within today's unpredictable open-world settings, especially in ubiquitous and pervasive computing settings, which demand techniques that let software react to changes by self-organizing its structure and self-adapting its behavior. The more we move toward dynamic and heterogeneous systems, and the more we stress their self-healing and self-adapting capabilities, the more we need new approaches to develop these applications and new ways to structure and program them. Programming open systems requires new programming language features. Two features that bear investigation are introspection mechanisms to get runtime information about newly encountered services and reflective mechanisms to adapt client applications dynamically. Some existing standards, industrial products, and research prototypes that support, to a certain extent, the open-world assumptions are service-oriented technologies, publish/subscribe middleware systems, grid infrastructures, autonomic frameworks     

Transparent autonomization in CORBA

Increasingly, software systems are constructed by integrating and composing multiple existing applications. The resulting complexity increases the need for self-management of the system. However, adding autonomic behavior to composite systems is difficult, especially when the constituent components are heterogeneous and they were not originally designed to support such interactions. Moreover, entangling the code for self-management with the code for the business logic of the original applications may actually increase the complexity of the systems, counter to the desired goal. In this paper, we address autonomization of composite systems that use CORBA, one of the first widely used middleware platforms introduced more than 17 years ago that is still commonly used in numerous systems. We propose a model, called Adaptive CORBA Template (ACT), that enables autonomic behavior to be added to CORBA applications automatically and transparently, that is, without requiring any modifications to the code implementing the business logic of the original applications. To do so, ACT uses “generic” interceptors, which are added to CORBA applications at startup time and enable autonomic behavior to be introduced later at runtime. We have developed ACT/J, a prototype of ACT in Java. We describe a case study in which ACT/J is used to introduce three types of autonomic behavior (self-healing, self-optimization, and self-configuration) to a distributed surveillance application.     

Achieving dynamic adaptation via management and interpretation of runtime models

In this article, we present a generic model-centric approach for realizing fine-grained dynamic adaptation in software systems by managing and interpreting graph-based models of software at runtime. We implemented this approach as the Graph-based Runtime Adaptation Framework (GRAF), which is particularly tailored to facilitate and simplify the process of evolving and adapting current software towards runtime adaptivity. As a proof of concept, we present case study results that show how to achieve runtime adaptivity with GRAF and sketch the framework's capabilities for facilitating the evolution of real-world applications towards self-adaptive software. The case studies also provide some details of the GRAF implementation and examine the usability and performance of the approach.