Improving Scalability and Reward of Utility-Driven Self-Healing for Large Dynamic Architectures

Abstract

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches. S. Ghahremani et al.approaches Kephart and Das 2007] combine both phases. Adaptation is executed for specific events and under specific conditions by adaptation rules. In such approaches, events trigger the rules that subsequently check their conditions. If the conditions are fulfilled, the actions of the rules are applied and result in the envisioned changes. Thus, the applicable rules are identified (matched) and executed to adapt the system configuration at runtime. The main strengths of such approaches are the readability, elegance, and the efficient processing of the rules. The drawbacks are the fact that the adaptation decisions are often only satisfying and the limited expressiveness of rules since rules typically just relate events to actions [Fleurey and Solberg 2009] without defining and performing any further computation for analysis and planning (e.g., to identify optimal actions). On the other hand, utility-driven approaches [Esfahani et al. 2013;Kephart and Walsh 2004] determine optimal adaptation decisions by using optimization techniques for planning that are guided by a utility function. A utility function determines how valuable each possible system configuration is, and the optimization aims at identifying optimal configurations. However, the optimization usually prevents the ...

Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements

“…In our empirical experiments we structure the spectrum of the considered self-healing approaches with respect to their employed planning schemes. The spectrum is limited to three self-healing approaches developed in our former work [43,58]:…”

“…The drop of the system utility caused by each type of failure is estimated at design-time, which leads to a fixed order in which the failure types are addressed by this approach. Since the approach is purely static, it does not add any runtime overhead due to planning for the repair of the failures (see [43,58]).…”

“…The tasks of assigning proper repair action to each failure and prioritizing them for execution are defined as optimization problems. As shown in [58], the approach is an optimal (concerning system utility) but expensive heuristic employing optimization-based planning, and suffers from a large runtime overhead. U-driven.…”

“…In the following we describe the Grid'5000 failure model, one of the probabilistic failure models presented in [58] which is fully fitted to real data and employed in the experiments of this study.…”

“…Uniform, Single, and Bigburst failure models are the three probabilistic failure models partially fitted to real data presented in [58] which are also considered in the experiments of this study. We extend the set in this paper by adding one more failure model to the set, referred to as Smallburst.…”