The design of a self-healing composition cycle for Web services

Chan, K. S. May; Bishop, Judith

doi:10.1109/seams.2009.5069070

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…To explicitly consider latency 4 Examples of such work are: Camara and de Lemos [2012]; Ehlers et al [2011]; Haupt [2012]; Neti and Mueller [2007]; Qun et al [2005]; Salehie and Tahvildari [2006]; Schmitt et al [2011]. 5 Such approaches either use observed and manually adjusted failure traces (e.g., Garlan and Schmerl [2002]; Haesevoets et al [2009]; Ippoliti and Zhou [2012]), probabilistic or simple random failure traces (e.g., Anaya et al [2014]; Chan and Bishop [2009]; Piel et al [2011]), or deterministic failure traces (e.g., Angelopoulos et al [2014]; Carzaniga et al [2008]; Casanova et al [2013]; Di Marco et al [2013]; Griffith et al [2009]; Hassan et al [2015]; Magalhaes and Silva [2015]; Perino [2013]). similarly to , the latency of each repair rule needs to be estimated and then added to the execution time of the rule.…”

Section: Related Workmentioning

confidence: 99%

“…Examples of approaches that use simulation to evaluate self-healing systems are:Anaya et al [2014];Angelopoulos et al [2014];Camara and de Lemos [2012];Carzaniga et al [2008];Casanova et al [2013];Chan and Bishop [2009]; DiMarco et al [2013];Ehlers et al [2011];Garlan and Schmerl [2002];Griffith et al [2009];Haesevoets et al [2009];Hassan et al [2015];Haupt [2012];Ippoliti and Zhou [2012];Magalhaes and Silva [2015];Neti and Mueller [2007];Perino [2013];Piel et al [2011];Qun et al [2005];Salehie and Tahvildari [2006];Schmitt et al [2011].ACM Trans. Autonom.…”

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confidence: 99%

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

Ghahremani

Giese

Vogel

2019

ACM Trans. Auton. Adapt. Syst.

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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 ...

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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

Ghahremani

Giese

Vogel

2019

ACM Trans. Auton. Adapt. Syst.

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“…There was a clear focus on run time instead of design time solutions [93], [243] as well as making systems dynamic in their configuration and adaptation [20], [31], [204]. Self-healing was an important part of web services research during this time, highlighting the use in workarounds [54] and composition cycles [57]. The RAINBOW framework was evaluated on a signature case study: Znn.com to assess the effectiveness of self-adaptation with good results [61].…”

Section: -2010 -Ramping Upmentioning

confidence: 99%

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

Wong¹,

Wagner²

2021

Preprint

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Championed by IBM's vision of autonomic computing paper in 2003, the autonomic computing research field has seen increased research activity over the last 20 years. Several conferences (SEAMS, SASO, ICAC) and workshops (SISSY) have been established and have contributed to the autonomic computing knowledge base in search of a new kind of system -a self-adaptive system (SAS). These systems are characterized by being context-aware and can act on that awareness. The actions carried out could be on the system or on the context (or environment). The underlying goal of a SAS is the sustained achievement of its goals despite changes in its environment. Despite a number of literature reviews on specific aspects of SASs ranging from their requirements to quality attributes, we lack a systematic understanding of the current state of the art. This paper contributes a systematic literature review into self-adaptive systems using the dblp computer science bibliography as a database. We filtered the records systematically in successive steps to arrive at 293 relevant papers. Each paper was critically analyzed and categorized into an attribute matrix. This matrix consisted of five categories, with each category having multiple attributes. The attributes of each paper, along with the summary of its contents formed the basis of the literature review that spanned 30 years . We characterize the maturation process of the research area from theoretical papers over practical implementations to more holistic and generic approaches, frameworks, and exemplars, applied to areas such as networking, web services, and robotics, with much of the recent work focusing on IoT and IaaS.

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“…FET is not recognized as a dimension for characterizing the employed failure traces. Six studies (17%) use a naive variation of probabilistic failure models not fitted to real data, which is random occurrences of failures to characterize the input for the SHS [44][45][46][47][48][49]. In these studies, the number of the failures (i.e., FGS) is deterministic, but the IAT and FET are randomly defined.…”

Section: Rq2mentioning

confidence: 99%

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

Ghahremani

Giese

2020

Computers

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Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions.

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The design of a self-healing composition cycle for Web services

Cited by 9 publications

References 15 publications

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

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

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

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

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