Andres J. Ramirez

Design patterns for developing dynamically adaptive systems

Increasingly, software systems should self-adapt to satisfy new requirements and environmental conditions that may arise after deployment. Due to their high complexity, adaptive programs are difficult to specify, design, verify, and validate. Moreover, the current lack of reusable design expertise that can be leveraged from one adaptive system to another further exacerbates the problem. We studied over thirty adaptation-related research and project implementations available from the literature and open sources to harvest adaptation-oriented design patterns that support the development of adaptive systems. These adaptation-oriented patterns facilitate the separate development of the functional and adaptive logic. In order to support the assurance of adaptive systems, each design pattern includes templates that formally specify invariant properties of adaptive systems. To demonstrate their usefulness, we have applied a subset of our adaptation-oriented patterns to the design and implementation of ZAP.com, an adaptive news web server.

A taxonomy of uncertainty for dynamically adaptive systems

Self-reconfiguration enables a dynamically adaptive system (DAS) to satisfy requirements even as detrimental system and environmental conditions arise. A DAS, especially one intertwined with physical elements, must increasingly reason about and cope with unpredictable events in its execution environment. Unfortunately, it is often infeasible for a human to exhaustively explore, anticipate, or resolve all possible system and environmental conditions that a DAS will encounter as it executes. While uncertainty can be difficult to define, its effects can hinder the adaptation capabilities of a DAS. The concept of uncertainty has been extensively explored by other scientific disciplines, such as economics, physics, and psychology. As such, the software engineering DAS community can benefit from leveraging, reusing, and refining such knowledge for developing a DAS. By synthesizing uncertainty concepts from other disciplines, this paper revisits the concept of uncertainty from the perspective of a DAS, proposes a taxonomy of potential sources of uncertainty at the requirements, design, and execution phases, and identifies existing techniques for mitigating specific types of uncertainty. This paper also introduces a template for describing different types of uncertainty, including fields such as source, occurrence, impact, and mitigating strategies. We use this template to describe each type of uncertainty and illustrate the uncertainty source in terms of an example DAS application from the intelligent vehicle systems (IVS) domain.

Applying genetic algorithms to decision making in autonomic computing systems

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve reconfiguration plans at run time in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, with the goal of minimizing costs while maximizing data reliability and network performance, even in the presence of link failures.

Automatically exploring how uncertainty impacts behavior of dynamically adaptive systems

A dynamically adaptive system (DAS) monitors itself and its execution environment to evaluate requirements satisfaction at run time. Unanticipated environmental conditions may produce sensory inputs that alter the self-assessment capabilities of a DAS in unpredictable and undesirable ways. Moreover, it is impossible for a human to know or enumerate all possible combinations of system and environmental conditions that a DAS may encounter throughout its lifetime. This paper introduces Loki, an approach for automatically discovering combinations of environmental conditions that produce requirements violations and latent behaviors in a DAS. By anticipating adverse environmental conditions that might arise at run time, Loki facilitates the identification of goals with inadequate obstacle mitigations or insufficient constraints to prevent such unwanted behaviors. We apply Loki to an autonomous vehicle system and describe several undesirable behaviors discovered.

Plato: a genetic algorithm approach to run-time reconfiguration in autonomic computing systems

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve target reconfigurations at run time that balance tradeoffs between functional and non-functional requirements in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, demonstrating an effective decision-making method for diffusing data and minimizing operational costs while maximizing data reliability and network performance, even in the presence of link failures.

Automatic derivation of utility functions for monitoring software requirements

Utility functions can be used to monitor requirements of a dynamically adaptive system (DAS). More specifically, a utility function maps monitoring information to a scalar value proportional to how well a requirement is satisfied. Utility functions may be manually elicited by requirements engineers, or indirectly inferred through statistical regression techniques. This paper presents a goal-based requirements modeldriven approach for automatically deriving state-, metric-, and fuzzy logic-based utility functions for RELAXed goal models. State- and fuzzy logic-based utility functions are responsible for detecting requirements violations, and metric-based utility functions are used to detect conditions conducive to a requirements violation. We demonstrate the proposed approach by applying it to the goal model of an intelligent vehicle system (IVS) and use the derived utility functions to monitor the IVS under different environmental conditions at run time.

Relaxing claims: coping with uncertainty while evaluating assumptions at run time

Self-adaptation enables software systems to respond to changing environmental contexts that may not be fully understood at design time. Designing a dynamically adaptive system (DAS) to cope with this uncertainty is challenging, as it is impractical during requirements analysis and design time to anticipate every environmental condition that the DAS may encounter. Previously, the RELAX language was proposed to make requirements more tolerant to environmental uncertainty, and Claims were applied as markers of uncertainty that document how design assumptions affect goals. This paper integrates these two techniques in order to assess the validity of Claims at run time while tolerating minor and unanticipated environmental conditions that can trigger adaptations. We apply the proposed approach to the dynamic reconfiguration of a remote data mirroring network that must diffuse data while minimizing costs and exposure to data loss. Results show RELAXing Claims enables a DAS to reduce adaptation costs.

Living with Uncertainty in the Age of Runtime Models

Uncertainty can be defined as the difference between information that is represented in an executing system and the information that is both measurable and available about the system at a certain point in its life-time. A software system can be exposed to multiple sources of uncertainty produced by, for example, ambiguous requirements and unpredictable execution environments. A runtime model is a dynamic knowledge base that abstracts useful information about the system, its operational context and the extent to which the system meets its stakeholders’ needs. A software system can successfully operate in multiple dynamic contexts by using runtime models that augment information available at design-time with information monitored at runtime. This chapter explores the role of runtime models as a means to cope with uncertainty. To this end, we introduce a well-suited terminology about models, runtime models and uncertainty and present a state-of-the-art summary on model-based techniques for addressing uncertainty both at development- and runtime. Using a case study about robot systems we discuss how current techniques and the MAPE-K loop can be used together to tackle uncertainty. Furthermore, we propose possible extensions of the MAPE-K loop architecture with runtime models to further handle uncertainty at runtime. The chapter concludes by identifying key challenges, and enabling technologies for using runtime models to address uncertainty, and also identifies closely related research communities that can foster ideas for resolving the challenges raised.

Towards run-time testing of dynamic adaptive systems

It is challenging to design, develop, and validate a dynamically adaptive system (DAS) that satisfies requirements, particularly when requirements can change at run time. Testing at design time can help verify and validate that a DAS satisfies its specified requirements and constraints. While offline tests may demonstrate that a DAS is capable of satisfying its requirements before deployment, a DAS may encounter unanticipated system and environmental conditions that can prevent it from achieving its objectives. In working towards a requirements-aware DAS, this paper proposes run-time monitoring and adaptation of tests as another technique for evaluating whether a DAS satisfies, or is even capable of satisfying, its requirements given its current execution context. To this end, this paper motivates the need and identifies challenges for adaptively testing a DAS at run time, as well as suggests possible methods for leveraging offline testing techniques for verifying run-time behavior.

Automatically RELAXing a goal model to cope with uncertainty

Dynamically adaptive systems (DAS) must cope with changing system and environmental conditions that may not have been fully understood or anticipated during development time. RELAX is a fuzzy logic-based specification language for making DAS requirements more tolerable to unanticipated environmental conditions. This paper presents AutoRELAX, an approach that generates RELAXed goal models that address environmental uncertainty by identifying which goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that defines the goal satisfaction criteria. AutoRELAX searches for RELAXed goal models that enable a DAS to satisfy its functional requirements while balancing tradeoffs between minimizing the number of RELAXed goals and minimizing the number of adaptations triggered by minor and adverse environmental conditions. We apply AutoRELAX to an industry-provided network application that self-reconfigures in response to adverse environmental conditions, such as link failures.