A Practical Management of Fuzzy Truth-Degrees Using FLOPER

Morcillo, Pedro J.; Moreno, Ginés; Penabad, Jaime; Vázquez, Carlos

doi:10.1007/978-3-642-16289-3_4

Cited by 27 publications

(12 citation statements)

References 18 publications

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“…In this section we firstly summarize the main features of the MALP language 1 , next we introduce the "Fuzzy LOgic Programming Environment for Research", F LOPER in brief, developed in our research group (see [26], [27] and visit http://dectau.uclm.es/floper/) and finally, we illustrate and formally prove the properties of our new MALP-based model of SSE according to Figure 2.…”

Section: Sse For/with Multi-adjoint Logic Programmingmentioning

confidence: 99%

“…As detailed in [28], [26], our parser has been implemented by using the classical DCG's (Definite Clause Grammars) resource of the PROLOG language, since it is a convenient notation for expressing grammar rules. Once the application is loaded inside a PROLOG interpreter (such as Sicstus or SWI), it shows a menu which includes options for loading/compiling, parsing, listing and saving fuzzy programs, as well as for executing/debugging goals and managing multi-adjoint lattices.…”

Section: B F Lopermentioning

confidence: 99%

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Fuzzy Logic Rules Modeling Similarity-based Strict Equality

Moreno

Penabad

Vázquez

2014

Annals of Computer Science and Information Systems

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Abstract-A classical, but even nowadays challenging research topic in declarative programming, consists in the design of powerful notions of "equality", as occurs with the flexible (fuzzy) and efficient (lazy) model that we have recently proposed for hybrid declarative languages amalgamating functional-fuzzylogic features. The crucial idea is that, by extending at a very low cost the notion of "strict equality" typically used in lazy functional (HASKELL) and functional-logic (CURRY) languages, and by relaxing it to the more flexible one of similarity-based equality used in modern fuzzy-logic programming languages (such as LIKELOG and BOUSI∼PROLOG), similarity relations can be successfully treated while mathematical functions are lazily evaluated at execution time. Now, we are concerned with the socalled Multi-Adjoint Logic Programming approach, MALP in brief, which can be seen as an enrichment of PROLOG based on weighted rules with a wide range of fuzzy connectives. In this work, we revisit our initial notion of SSE (Similarity-based Strict Equality) in order to re-model it at a very high abstraction level by means of a simple set of MALP rules. The resulting technique (which can be tested on-line in dectau.uclm.es/sse) not only simulates, but also surpass in our target framework, the effects obtained in other fuzzy logic languages based on similarity relations (with much more complex/reinforced unification algorithms in the core of their procedural principles), even when the current operational semantics of MALP relies on the simpler, purely syntactic unification method of PROLOG.

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Section: Sse For/with Multi-adjoint Logic Programmingmentioning

confidence: 99%

Section: B F Lopermentioning

confidence: 99%

Fuzzy Logic Rules Modeling Similarity-based Strict Equality

Moreno

Penabad

Vázquez

2014

Annals of Computer Science and Information Systems

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“…These predicates are defined in rules R 2 to R 13 for four cities (Madrid, Istambul, Tokyo and Baku), in such a way that, for each city, the feature modeled by each predicate is better the greater the truth value of the rule. In order to run and manage MALP programs, during the last years we have designed the FLOPER (Fuzzy LOgic Programming Environment for Research) system [16,15,17,18], which is freely accessible online [10]. The parser of our tool has been implemented by using the classical DCG's (Definite Clause Grammars) resource of the Prolog language, since it is a convenient notation for expressing grammar rules.…”

Section: The Multi-adjoint Logic Language and Flopermentioning

confidence: 99%

A Fuzzy Approach to Cloud Admission Control for Safe Overbooking

Vázquez

Tomás

Moreno

et al. 2013

Fuzzy Logic and Applications

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A fuzzy approach to cloud admission control for safe overbooking. (pp. 212-225 Abstract. Cloud computing enables elasticity -rapid provisioning and deprovisioning of computational resources. Elasticity allows cloud users to quickly adapt resource allocation to meet changes in their workloads. For cloud providers, elasticity complicates capacity management as the amount of resources that can be requested by users is unknown and can vary significantly over time. Overbooking techniques allow providers to increase utilization of their data centers. For safe overbooking, cloud providers need admission control mechanisms to handle the tradeoff between increased utilization (and revenue), and risk of exhausting resources, potentially resulting in penalty fees and/or lost customers. We propose a flexible approach (implemented with fuzzy logic programming) to admission control and the associated risk estimation. Our measures exploit different fuzzy logic operators in order to model optimistic, realistic, and pessimistic behaviour under uncertainty. The application has been coded with the MALP language by using the FLOPER system developed in our research group. An experimental evaluation confirm that our fuzzy admission control approach can significantly increase resource utilization while minimizing the risk of exceeding the total available capacity. In: 10th International Workshop on Fuzzy Logic and Applications (WILF)

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“…A summary of all these steps performed to obtain the final pessimistic, realistic and optimistic risk values is detailed in Algorithm 1. Our fuzzy framework is based on Multi-Adjoint Logic Programming (MALP) [9], [10] and implemented done using the FLOPER (Fuzzy LOgic Programming Environment for Research) system [11].…”

Section: A Fuzzy Risk Aggregationmentioning

confidence: 99%

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

Tomás

Tordsson

2013

2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing

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Abstract-Elasticity is key for the cloud paradigm, where the pay-per use nature provides great flexibility for end-users. However, elasticity complicates long-term capacity planning for cloud providers as the exact amount of resources required over time becomes uncertain. Admission control techniques are thus needed to handle the trade-off between resource utilization and potential overload. We define a set of admission control algorithms that combine risk assessment methods with a fuzzy aggregation framework. An experimental evaluation using a mixture of bursty and steady applications demonstrate that our algorithms can increase resource utilization by a factor of two while limiting overload problems to a few percent.

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A Practical Management of Fuzzy Truth-Degrees Using FLOPER

Cited by 27 publications

References 18 publications

Fuzzy Logic Rules Modeling Similarity-based Strict Equality

Fuzzy Logic Rules Modeling Similarity-based Strict Equality

A Fuzzy Approach to Cloud Admission Control for Safe Overbooking

Cloudy with a Chance of Load Spikes: Admission Control with Fuzzy Risk Assessments

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