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See detailComparisons of Heat Map and IFL Technique to Evaluate the Performance of Commercially Available Cloud Providers
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL et al

in Comparisons of Heat Map and IFL Technique to Evaluate the Performance of Commercially Available Cloud Providers (in press)

Cloud service providers (CSPs) offer different Ser- vice Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to ... [more ▼]

Cloud service providers (CSPs) offer different Ser- vice Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to users requirements. Generally, a CSB considers the service commitments of CSPs rather than the actual quality of CSPs services. To overcome this issue, the broker should verify the service performances while recommending cloud services to the cloud users, using all available data. In this paper, we compare our two approaches to do so: a min-max-min decomposition based on Intuitionistic Fuzzy Logic (IFL) and a Performance Heat Map technique, to evaluate the performance of commercially available cloud providers. While the IFL technique provides simple, total order of the evaluated CSPs, Performance Heat Map provides transparent and explanatory, yet consistent evaluation of service performance of commercially available CSPs. The identified drawbacks of the IFL technique are: 1) It does not return the accurate performance evaluation over multiple decision alternatives due to highly influenced by critical feedback of the evaluators; 2) Overall ranking of the CSPs is not as expected according to the performance measurement. As a result, we recommend to use performance Heat Map for this problem. [less ▲]

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See detailAlgorithmic Decision Theory for solving complex decision problems
Bisdorff, Raymond UL

Presentation (2017, May 03)

Today's decision makers in fields ranging from engineering to psychology, from medicine to economics and/or homeland security are faced with remarkable new technologies, huge amounts of information to ... [more ▼]

Today's decision makers in fields ranging from engineering to psychology, from medicine to economics and/or homeland security are faced with remarkable new technologies, huge amounts of information to help them in reaching good decisions, and the ability to share information at unprecedented speeds and quantities. These tools and resources should lead to better decisions. Yet, the tools bring with them daunting new problems: the massive amounts of data available are often incomplete, unreliable and/or distributed and there is great uncertainty in them; interoperating/distributed decision makers and decision making devices need to be coordinated; many sources of data need to be fused into a good decision; information sharing under new cooperation/competition arrangements raises security problems. When faced with such issues, there are few highly efficient algorithms available to support decision makers. The objective of Algorithmic Decision Theory (ADT) is to improve the ability of decision makers to perform well when facing these new challenges and problems through the use of methods from theoretical computer science, in particular algorithmic methods. The primary goal of ADT is hence to explore and develop algorithmic approaches for solving decision problems arising in a variety of applications areas. Examples include, but are not limited to: - Computational tractability/intractability of social consensus and multiple criteria compromise functions; - Improvement of decision support and recommender systems; - Development of automatic decision devices including on-line decision procedures; - Robust decision making; - Learning for multi-agent systems and other on-line decision devices. This presentation will focus more specifically on multiple criteria decision aiding methodology, the actual research field of the author. [less ▲]

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See detailComputing linear rankings from trillions of pairwise outranking situations
Bisdorff, Raymond UL

Scientific Conference (2016, November)

We present in this paper a sparse HPC implementation for outranking digraphs of huge orders, up to several millions of decision alternatives. The proposed outranking digraph model is based on a quantiles ... [more ▼]

We present in this paper a sparse HPC implementation for outranking digraphs of huge orders, up to several millions of decision alternatives. The proposed outranking digraph model is based on a quantiles equivalence class decomposition of the underlying multicriteria performance tableau. When locally ranking each of these ordered components, we may readily obtain an overall linear ranking of big sets of decision alternatives. For the local rankings, both, Copeland's as well as the Net-Flows ranking rules, appear to give the best compromise between, on the one side, the fitness of the overall ranking with respect to the given global outranking relation and, on the other side, computational tractability for very big outranking digraphs modelling up to several trillions of pairwise outranking situations. [less ▲]

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See detailA sparse outranking digraph model for HPC-ranking of big performance tableaux
Bisdorff, Raymond UL

Scientific Conference (2016, July 05)

In the context of the ongoing GDRI-Algodec "Algorithmic Decision Theory", supported o.a. by the CNRS (France) and the FNR (Luxembourg), we develop multicriteria ranking HPC algorithms for large sets of ... [more ▼]

In the context of the ongoing GDRI-Algodec "Algorithmic Decision Theory", supported o.a. by the CNRS (France) and the FNR (Luxembourg), we develop multicriteria ranking HPC algorithms for large sets of potential decision alternatives: up to several thousand of alternatives evaluated on multiple incommensurable ordinal performance criteria. By using Python3.5 multiprocessing resources and the Digraph3 multicriteria software library, we could linearly rank without ties on the UL HPC gaia-80 machine with 120 single threaded cores and a CPU memory of 2.3 TB, in about four hours (6h05') a huge set of 1'732'051 decision alternatives evaluated on 13 performance criteria by balancing economic, ecological and societal decision objectives. Data input is, on the one side, a 1'732'051 x 13 performance tableau of size 2.6GB, and on the other side, a theoretical outranking space consisting of three trillions (3 x 1012) of pairwise outranking situations. A "small" set of 1000 decision alternatives, in a similar setting, may thus be ranked typically in about 2 seconds. [less ▲]

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See detailRanking big performance tableaux with multiple incommensurable criteria and missing data
Bisdorff, Raymond UL

Scientific Conference (2016, January)

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See detailAn Evaluation Model for Selecting Cloud Services from Commercially Available Cloud Providers
Wagle, Shyam Sharan UL; Guzek, Mateusz UL; Bouvry, Pascal UL et al

in 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom) (2015, December)

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See detailOn boosting Kohler's ranking-by-choosing rule with a quantiles preordering
Bisdorff, Raymond UL

Scientific Conference (2015, February 06)

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See detailIntroduction
Bisdorff, Raymond UL; Dias, L. C.; Meyer, P. et al

Book published by Springer (2015)

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See detailThe EURO 2004 best poster award: Choosing the best poster in a scientific conference
Bisdorff, Raymond UL

Book published by Springer (2015)

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See detailEvaluation and decision models with multiple criteria: Case studies
Bisdorff, Raymond UL; Dias, L. C.; Meyer, P. et al

Book published by Springer (2015)

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See detailOn confident outrankings with multiple criteria of uncertain significance
Bisdorff, Raymond UL

Scientific Conference (2014, November 11)

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See detailOn ranking-by-choosing with bipolar outranking digraphs of large orders
Bisdorff, Raymond UL

Scientific Conference (2014, October 27)

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See detailOn weakly ordering by choosing from valued pairwise outranking situations
Bisdorff, Raymond UL

Scientific Conference (2014, January 31)

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See detailTutorials of the Digraph3 resources
Bisdorff, Raymond UL

Software (2014)

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See detailElicitation of criteria weights maximising the stability of pairwise outranking statements
Bisdorff, Raymond UL; Meyer, P.; Veneziano, T.

in Journal of Multi-Criteria Decision Analysis (2014), 21(1-2), 113-124

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See detailThe Electre like outranking approach to MCDA
Bisdorff, Raymond UL

Presentation (2013, August)

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See detailOn polarizing outranking relations with large performance differences
Bisdorff, Raymond UL

in Journal of Multi-Criteria Decision Analysis (2013), 20

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See detailDescriptive profiles for sets of alternatives in multiple criteria decision aid
Olteanu, A.-L.; Meyer, P.; Bisdorff, Raymond UL

in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2013), 8176 LNAI

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See detailAlgorithmic Decision Theory
Bisdorff, Raymond UL

Scientific Conference (2012, December)

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See detailOn measuring and testing the ordinal correlation between bipolar outranking relations
Bisdorff, Raymond UL

Scientific Conference (2012, November 15)

Detailed reference viewed: 11 (3 UL)