Optimal planning for architecture-based self-adaptation via model checking of stochastic games

Cámara, Javier; Garlan, David; Schmerl, Bradley; Pandey, Ashutosh

doi:10.1145/2695664.2695680

Cited by 42 publications

(30 citation statements)

References 21 publications

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“…Unfairness at iteration τ w : R d → R Preference weight; raises the cost of disliked plans λ ∈ R Controls the tradeoff between global and local cost ρ i,a ∈ R Dislike of plan i by agent a plan as one of three possible plans. Plan generation can be performed with various methodologies that include clustering (Pournaras et al 2014a), classification (Fröhling 2017), Markov decision processes (Pandey et al 2016), or model checking of stochastic multiplayer games (Cámara et al 2015).…”

Section: Combinatorial Optimizationmentioning

confidence: 99%

Decentralized Collective Learning for Self-managed Sharing Economies

Pournaras

Pilgerstorfer

Asikis

2018

ACM Trans. Auton. Adapt. Syst.

130

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The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens' autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.

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Section: Combinatorial Optimizationmentioning

confidence: 99%

Decentralized Collective Learning for Self-managed Sharing Economies

Pournaras

Pilgerstorfer

Asikis

2018

ACM Trans. Auton. Adapt. Syst.

130

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“…Such learning-based approaches suffer from a slow learning curve and achieve suboptimal utility or QoS during the time when the learning has not converged yet. Furthermore, probabilistic model checking has been used to solve complex optimization problems at runtime [Cámara et al 2015Sykes et al 2007]. The time complexity of model checking typically results in solutions that do not scale for large configuration spaces and that cannot be applied in systems requiring instantaneous adaptation decisions.…”

Section: Related Workmentioning

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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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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“…Discrete-time Markov chains [14], [22], [42], [44], [45], [55] PCTL a , LTL b , PCTL* c Markov decision processes [48] PCTL a , LTL b , PCTL* c Probabilistic automata [20], [66] PCTL a , LTL b , PCTL* c Continuous-time Markov chains [18], [21], [52] CSL d Stochastic games [25], [26] rPATL e a Probabilistic Computation Tree Logic [8], [57] b Linear Temporal Logic [84] c PCTL* is a superset of PCTL and LTL d Continuous Stochastic Logic [3], [4] e reward-extended Probabilistic Alternating-time Temporal Logic [27] reading a new UUV-sensor measurement rate (in process()) and checking whether this rate has changed to such extent that a new analysis is required (in analysisRequired()). If analysis is required, the analyzer automaton sends a verify!…”

Section: Type Of Stochastic Model Non-functional Requirement Specificmentioning

confidence: 99%

Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases

Călinescu

Weyns

Gerasimou

et al. 2018

IIEEE Trans. Software Eng.

158

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Abstract-Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.

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Optimal planning for architecture-based self-adaptation via model checking of stochastic games

Cited by 42 publications

References 21 publications

Decentralized Collective Learning for Self-managed Sharing Economies

Decentralized Collective Learning for Self-managed Sharing Economies

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

Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases

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