Chun Cao

Adam: Identifying defects in context-aware adaptation

Context-aware applications, as a typical type of self-adaptive software systems, are receiving increasing attention. These applications continually adapt to environmental changes in an autonomic way. However, their adaptation may contain defects when the complexity of modeling all environmental changes is beyond a developers ability. Such defects can cause failure to the adaptation and result in application crash or freezing. Relating these failures back to responsible defects is challenging. In this paper we propose a novel approach, called Adam, to assist identifying defects in the context-aware adaptation. Adam monitors runtime errors for an application, logs relevant error information, and relates them to responsible defects in this application. To make our Adam approach feasible, we investigate the error types that are commonly exhibited by various failures reported in context-aware applications. Adam detects these errors in order to identify responsible defects in context-aware applications. To detect these errors, Adam formally models the adaptation semantics for context-aware applications, and integrates into them a set of assertion checkers with respect to these error types. We experimentally evaluated Adam through three context-aware applications. The experiments reported promising results that Adam can effectively detect errors, identify their responsible defects in applications, and give useful hints on how these defects can be fixed.

On Environment-Driven Software Model for Internetware

Internetware is envisioned as a general software paradigm for the application style of resources integration and sharing in the open, dynamic and uncertain platforms such as the Internet. Continuing the agent-based Internetware model presented in a previous paper, in this paper, after an analysis of the behavioral patterns and the technical challenges of environment-driven applications, a software-structuring model is proposed for environment-driven Internetware applications. A series of explorations on the enabling techniques for the model, especially the modeling, management and utilization of context information are presented. Several prototypical systems have also been built to prove the concepts and evaluate the techniques. These research efforts make a further step toward the Internetware paradigm by providing an initial framework for the construction of context-aware and self-adaptive software application systems in the open network environment.

Internetware: a shift of software paradigm

Internetware is envisioned as a new software paradigm for resource integration and sharing in the open, dynamic and autonomous network environment. In this paper we discuss our visions and explorations of this new paradigm, with focus placed on the methodological perspective. A set of enabling techniques on flexible coordination of autonomous services, automatic adaptation to changing environment and trust management-based assurance of dependability are proposed to help the development of Internetware applications.

Javelus: A Low Disruptive Approach to Dynamic Software Updates

Practical software systems are subject to frequent updates for fixing their bugs or addressing new requirements. Updating a software system without stopping and restarting it is desired, as this helps reduce the redeployment cost as well as achieving the high availability. Existing techniques for dynamically updating Java programs may introduce noticeable pauses during which these programs are unable to function. We in this paper present Javelus, a dynamic Java update system with greatly reduced pausing time but without sacrificing update flexibility and system efficiency. Different from previous approaches, Javelus uses a lazy update mechanism with which an object-to-update will not be updated until it is really used. We implemented Javelus on top of an industry-strength OpenJDK HotSpot VM. We evaluated Javelus with real updates to Tomcat 7 and the same micro array benchmark used in evaluating Jvolve and DCE VM. The experiments report promising results that Javelus only incurred a pausing time two orders of magnitude smaller than those of Jvolve and DCE VM.

Low-disruptive dynamic updating of Java applications

Context In-use software systems are destined to change in order to fix bugs or add new features. Shutting down a running system before updating it is a normal practice, but the service unavailability can be annoying and sometimes unacceptable. Dynamic software updating (DSU) migrates a running software system to a new version without stopping it. State-of-the-art Java DSU systems are unsatisfactory as they may cause a non-negligible system pause during updating. Objective In this paper we present Javelus, a Java HotSpot VM-based Java DSU system with very short pausing time. Method Instead of updating everything at once when the running application is suspended, Javelus only updates the changed code during the suspension, and migrates stale objects on-demand after the application is resumed. With a careful design this lazy approach neither sacrifices the update flexibility nor introduces unnecessary object validity checks or access indirections. Results Evaluation experiments show that Javelus can reduce the updating pausing time by one to two orders of magnitude without introducing observable overheads before and after the dynamic updating. Conclusion Our experience with Javelus indicates that low-disruptive and type-safe dynamic updating of Java applications can be practically achieved with a lazy updating approach.

Towards context consistency by concurrent checking for Internetware applications

Internetware applications are emerging and being widely used. They can adapt their behavior based on environmental contexts and deliver smart services. These contexts can be subject to various noises, which cause them to be inaccurate, incomplete, or even to conflict with each other. This is known as context inconsistency problem. Context inconsistency can trigger unexpected behavior to applications, and therefore should be prevented. One promising approach is to check contexts against consistency constraints so as to detect the occurrences of context inconsistency at runtime. Existing techniques have attempted different ways to improve the checking efficiency or effectiveness with different trade-offs in space overhead or communication cost. However, none of them has exploited multi-core computing capability to systematically improve the checking efficiency. In this paper, we propose a novel concurrent checking technique Con-C to efficiently detect inconsistencies in huge volumes of dynamic contexts. Con-C derives checking subtasks for each consistency constraint based on its structure and semantics. It achieves this in a fully automated way, and at the same time can guarantee its derived checking subtasks to be persistently balanced. We evaluated Con-C by controlled experiments through a large-scale real-world application. It reported promising results that Con-C improved the checking efficiency by extra 57.0%, in addition to what had been gained by incremental checking.

Minimizing the Side Effect of Context Inconsistency Resolution for Ubiquitous Computing

Applications in ubiquitous computing adapt their behavior based on contexts. The adaptation can be faulty if the contexts are subject to inconsistency. Various techniques have been proposed to identify key contexts from inconsistencies. By removing these contexts, an application is expected to run with inconsistencies resolved. However, existing practice largely overlooks an application’s internal requirements on using these contexts for adaptation. It may lead to unexpected side effect from inconsistency resolution. This paper studies a novel way of resolving context inconsistency with the aim of minimizing such side effect for an application. We model and analyze the side effect for rule-based ubiquitous applications, and experimentally measure and compare it for various inconsistency resolution strategies. We confirm the significance of such side effect if not controlled, and present an efficient framework to minimize it during context inconsistency resolution.

Verifying self-adaptive applications suffering uncertainty

Self-adaptive applications address environmental dynamics systematically. They can be faulty and exhibit runtime errors when environmental dynamics are not considered adequately. It becomes more severe when uncertainty exists in their sensing and adaptation to environments. Existing work verifies self-adaptive applications, but does not explicitly consider environmental constraints or uncertainty. This gives rise to inaccurate verification results. In this paper, we address this problem by proposing a novel approach to verifying self-adaptive applications suffering uncertainty in their environmental interactions. It builds Interactive State Machine (ISM) models for such applications and verifies them with explicit consideration of environmental constraints and uncertainty. It then refines verification results by prioritizing counterexamples according to their probabilities. We experimentally evaluated our approach with real-life self-adaptive applications, and the experimental results confirmed its effectiveness. Our approach reported 200-660% more counterexamples than not considering uncertainty, and eliminated all false counterexamples caused by ignoring environmental constraints.

Environment rematching: toward dependability improvement for self-adaptive applications

Self-adaptive applications can easily contain faults. Existing approaches detect faults, but can still leave some undetected and manifesting into failures at runtime. In this paper, we study the correlation between occurrences of application failure and those of consistency failure. We propose fixing consistency failure to reduce application failure at runtime. We name this environment rematching, which can systematically reconnect a self-adaptive application to its environment in a consistent way. We also propose enforcing atomicity for application semantics during the rematching to avoid its side effect. We evaluated our approach using 12 self-adaptive robot-car applications by both simulated and real experiments. The experimental results confirmed our approachs effectiveness in improving dependability for all applications by 12.5-52.5%.

Resynchronizing Model-Based Self-Adaptive Systems with Environments

Self-adaptive systems are attractive due to their ability of adapting to changeable environments automatically. However, such systems may be subject to runtime failures when all environmental dynamics cannot be adequately considered at design time. When such failures occur at runtime, a systems internal adaptation logic usually has become inconsistent with its environment, according to our observation. We call this inconsistency sync-loss error. From our project experiences, we empirically identified a strong correlation between sync-loss error and system failure. This motivated us to fix sync-loss error in order to reduce failure for self-adaptive systems. In this paper, we formulate the problem of detecting sync-loss error, and present a framework ReSync to automatically fix sync-loss errors by desynchronizing a system with its environment. We experimentally evaluated ReSync on real robot cars with 20 different system versions. The evaluation reported promising results that ReSync can automatically recover our robot car systems from sync-loss errors, and significantly reduce the failure rate from 90.9% to 11.7-28.8%.