An-Cheng Huang

Bio: An-Cheng Huang is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Software architecture & Adaptation (computer science). The author has an hindex of 5, co-authored 7 publications receiving 1939 citations.

Rainbow: architecture-based self-adaptation with reusable infrastructure

Abstract: Software-based systems today are increasingly expected to dynamically self-adapt to accommodate resource variability, changing user needs, and system faults. Recent work uses closed-loop control based on external models to monitor and adapt system behavior at run time. Taking this externalized approach, the Rainbow framework we have developed uses software architectural models to dynamically monitor and adapt a running system. A key goal and primary challenge of this framework is to support the reuse of adaptation strategies and infrastructure across different systems. We show that the separation of a generic adaptation infrastructure from system-specific adaptation knowledge makes this reuse possible.

Rainbow: architecture-based self-adaptation with reusable infrastructure

Abstract: While attractive in principle, architecture-based self-adaptation raises a number of research and engineering challenges. First, the ability to handle a wide variety of systems must be addressed. Second, the need to reduce costs in adding external control to a system must be addressed. Our rainbow framework attempts to address both problems. By adopting an architecture-based approach, it provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems. The specialization mechanisms let the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.

An architecture for coordinating multiple self-management systems

Abstract: A common approach to adding self-management capabilities to a system is to provide one or more external control modules, whose responsibility is to monitor system behavior, and adapt the system at run time to achieve various goals (configure the system, improve performance, recover from faults, etc.). An important problem arises when there is more than one such self-management module: how can one make sure that they are composed to provide consistent and complementary benefits? In this paper we describe a solution that introduces a self-management coordination architecture and infrastructure to support such composition. We focus on the problem of coordinating self-configuring and self-healing capabilities, particularly with respect to global configuration and incremental repair. We illustrate the approach in the context of a self-managing video teleconference system that composes two preexisting adaptation modules to achieve synergistic benefits of both.

Network-sensitive service discovery

Abstract: We consider the problem of network-sensitive service selection (NSSS): finding services that match a particular set of functional and network properties. Current solutions handle this problem using a two-step process. First, a user obtains a list of candidates through service discovery. Then, the user applies a network-sensitive server selection technique to find the best service. Such approaches are complex and expensive since each user has to solve the NSSS problem independently. In this paper, we present a simple alternative: network-sensitive service discovery (NSSD). By integrating network-sensitivity into the service discovery process, NSSD allows users who are looking for services to specify both the desired functional and network properties at the same time. Users benefit since they only have to solve a restricted version of the server selection problem. Moreover, NSSD can solve the NSSS problem more efficiently by amortizing the overhead over many users. We present the design of NSSD, a prototype implementation, and experimental results that illustrate how NSSD can be utilized for different applications.

Building self-adapting services using service-specific knowledge

Abstract: With the advances in middleware and Web services technologies, network sendees are evolving from simple client-sender applications to self-configuring services that can compose primitive components distributed in the Internet into a value-added service configuration that provides rich functionalities to users. A resulting research problem is how to continuously adapt such composite service configurations at run time in order to cope with the increasingly dynamic and heterogeneous network environments and computing platforms. In this paper, we propose a self-adaptation architecture that allows service developers to specify their service-specific adaptation knowledge as "externalized" adaptation strategies. These adaptation strategies are used by a general, shared adaptation framework to perform run-time adaptation operations that automatically incorporate service-specific knowledge. In addition to the strategies, we also identify another aspect of adaptation knowledge that is not addressed by previous solutions: adaptation coordination. Our framework provides integrated support for the specification and execution of both aspects of developers' adaptation knowledge.

Self-adaptive software: Landscape and research challenges

Abstract: Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

Rainbow: architecture-based self-adaptation with reusable infrastructure

Abstract: Software-based systems today are increasingly expected to dynamically self-adapt to accommodate resource variability, changing user needs, and system faults. Recent work uses closed-loop control based on external models to monitor and adapt system behavior at run time. Taking this externalized approach, the Rainbow framework we have developed uses software architectural models to dynamically monitor and adapt a running system. A key goal and primary challenge of this framework is to support the reuse of adaptation strategies and infrastructure across different systems. We show that the separation of a generic adaptation infrastructure from system-specific adaptation knowledge makes this reuse possible.

Rainbow: architecture-based self-adaptation with reusable infrastructure

Abstract: While attractive in principle, architecture-based self-adaptation raises a number of research and engineering challenges. First, the ability to handle a wide variety of systems must be addressed. Second, the need to reduce costs in adding external control to a system must be addressed. Our rainbow framework attempts to address both problems. By adopting an architecture-based approach, it provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems. The specialization mechanisms let the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.

Software Engineering for Self-Adaptive Systems : A Second Research Roadmap

Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Models@ Run.time to Support Dynamic Adaptation

Abstract: Today's society increasingly depends on software systems deployed in large companies, banks, airports, and so on. These systems must be available 24/7 and continuously adapt to varying environmental conditions and requirements. Such dynamically adaptive systems exhibit degrees of variability that depend on user needs and runtime fluctuations in their contexts. The paper presents an approach for specifying and executing dynamically adaptive software systems that combines model-driven and aspect-oriented techniques to help engineers tame the complexity of such systems while offering a high degree of automation and validation.