Actas JP2011 - Universidad de La Laguna

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Actas XXII Jornadas de Paralelismo (JP2011) , La Laguna, Tenerife, 7-9 septiembre 2011ing a qualitative approach to obtain a global visionof the impact of faults on ad hoc routing protocols,the core components of ad hoc networks [4]. In thispaper we take a step beyond to ease the hierarchicalanalysis of resilience benchmarking results by meansof a mathematical-based technique called Logic Scoreof Preferences (LSP). Introduced by Dujmović in [5],LSP is a fuzzy-logic-based approach that computesthe global score of a component through measuresaggregation, easing the hierarchical analysis of thesystem characteristics.Although this technique has been successfully usedfor the quantitative quality evaluation of a wide varietyof software engineering products (ranging fromsearch engines to web browsers), this is the first attempt,to the best of our knowledge, of applying thistechnique in the domain of resilience benchmarkingand ad hoc networks.The rest of this paper is structured as follows. SectionII reports the different alternatives for measuresaggregation and analysis. Secion III introduces theLSP technique. Section IV applies the LSP techniqueto analyse and interpret the results obtainedfrom a case study where different ad hoc routing protocolsare benchmarked. Finally Section V presentsconclusions.II. Measures-Aggregation ApproachesThe literature offers different graphic and analyticalternatives to synthesise the measures obtained duringthe evaluation of a target system.Kiviat or radar diagrams [6] are a graphical toolwhich represent the results of the benchmark inan easy-to-interpret footprint. Kiviat diagrams canshow different measures using only one diagram and,although some training is required, the comparisonof different diagrams is fairly simple. The scalabilityof Kiviat diagrams enables the representation ofup to tens of measures. However, managing such ahuge amount of information may make difficult theinterpretation and analysis of results.The problem previously stated is solved in [6]throughout the use of an analytical technique namedthe figure of merit, which imposing certain restrictionsto the graph axes, synthesises all the measuresinto a unique value related to the footprint shape.However, the problem associated to this solution, asit happens with most techniques using the mean orthe median, is that valuable information could behidden behind a unique number, and consequently,the comparison between protocols could result quitevague [7].Other approaches, like the presented in [8], characterisethe level of goodness of the measures accordingto their ability to fit with a particular statisticaldistribution. Nevertheless, this approach presentstwo main drawbacks. First, it assumes that a measurefollows the same distribution for all the systems,which may not be true depending on the context ofuse. And second, to understand this type of characterisation,it is necessary to understand the assumedstatistical model, which is not straightforward.Finally, other authors, like Al-Sbou [9], proposethe use of custom formulas for the aggregation ofmeasures obtaining a single score which characterisesthe behaviour of the system. However, these typesof formula definition is based on heuristics and lacksformal foundation and validation.In sum, these methodologies lack the ability of aggregatingmeasures into a meaningful result that: i)is easy to explain and interpret; ii) is representativeof real systems and allows their comparison and ranking,and iii) captures enough information to enablethe hierarchical analysis of measures from coarse tofine-grained measures (and vice-versa).III. Logic Score of PreferencesThe LSP technique computes the global score of asystem through the recursive decomposition of theircharacteristics into subcharacteristics and so on, untilobtaining quantifiable attributes (or measures).However, what makes it interesting with respect toto the rest of approaches presented in Section II accordinga resilience benchmarking viewpoint, is itscapability to navigate from the fine-grained measuresto the coarse-grained scores, without losing the numericalviewpoint of results. Thus, keeping the consistencyin the interpretation and analysis of resultsindependently from the viewpoint (fine or coarse) acquiredby the benchmark user.In general terms, LSP computes the global score(S) of a system using Formula 1.S = (k∑w i s r i ) 1 r /i=1k∑w i = 1 (1)i=1In this formula, each s i represents an elementaryscore (referred to as elementary preference) relatedto each (sub)characteristic of the targeted system.Different criterion functions specify how to quantitativelyevaluate each attribute, i.e., they establishan equivalence between the attribute value and thesystem quality requirements. To cope with this goal,the value of the attribute is scored within a 0-to-100scale, which results in an elementary preference scores i that can be interpreted as the degree of satisfactionof an attribute a i with respect to the qualityrequirements specified by the benchmark performerfor such attribute. Since all the attributes are scoredaccording to the same scale, resulting elementarypreferences are directly comparable. Such equivalencecan be mapped to a discrete function, thusestablishing different quality levels, or to a continuousone. Additionally, as far as the k elementarypreferences that compound the aggregation block definedby each characteristic may not have the sameimportance, a weight w i , illustrating such influence,must be assigned to each elementary preference. Inthis line, it is also necessary to define the degree ofmandatoriness that must be fulfilled for each aggregationblock. The power r, described in detail in[5], represents one logic operator in charge of definingthe type of relationship (from orness to andness)JP2011-374
Actas XXII Jornadas de Paralelismo (JP2011) , La Laguna, Tenerife, 7-9 septiembre 2011required for the different elementary scores withinthe same aggregation block. In [5], the author definesup to 20 different logic operators which describea mandatoriness------------------------------------------------- Operation Symbolr2r3r4r5gradation among the requirementsof the system. Such gradation ranges from the fullconjunction (logic AND) which illustrates the simultaneityamong all the requirements, to the full disjunction(logic OR) which represents the notion ofreplaceability,SQUAREMEAN DISJUNCTION D+infty+infty+infty+inftyWEAKQD(-) WEAKQD(+) MEDIUMQD STRONGQD(-)D+-5.8026.6757.3167.819STRONGQD(+)D++20.63024.30027.11030.090 DA3.9294.4504.8255.111 D+9.52111.09512.27013.235where meeting just one requirement isenoughARITHMETICMEANA1.0001.0001.0001.000D--1.4491.5191.5651.596 D-+2.7923.1013.3183.479 SQU2.000 D-2.0182.1872.3022.384(see Figure 1).MEDIUMQC STRONGQC(-)C+--1.655-1.550-1.455-1.380 HARMONICMEANHAR-1.000 WEAKQC(+) GEOMETRICMEANGEO0.000 WEAKQC(-) C-+-0.148-0.208-0.235-0.251 CA-0.720-0.732-0.721-0.707 C--0.6190.5730.5460.526STRONGQC(+)C++-9.060-7.639-6.689-6.013 C+-3.510-3.114-2.823-2.606 C-0.2610.1920.1530.129CONJUNCTION C-infty-infty-infty-inftyFig. 1. Aggregation operators proposed by Dujmović, and rvalue for 2, 3, 4 and 5 inputsOnce all the intermediate scores have been computeduntil obtaining a global score, a coarse-grainedanalysis of each target can be performed. Then, theanalysis can be progressively refined using the availableintermediate scores until considering the originalbenchmarking results. The benefit of using LSP relieson the fact that it systematises the way in whichscores are obtained from measures and naturally establishesa hierarchical approach for their analysis.The main concepts of LSP are illustrated throughoutFigure 2.Attributes(Benchmark measures)Fig. 2.AggregationAnalysisW·SW·SW·SW·SMeasures are aggregatedattending to a relationshipestrablished by thebenchmark performerW·SW·SS: Intermediate scoresRepresentation of the LSP techniqueIV. Case StudyAs already stated, LSP is a technique that can beapplied to any type of system or component. TheGlobal Scorecase study proposed in this paper emulates the deploymentof a Wireless Mesh Network (WMN) [10],one of the most extended types of ad hoc network.A. Experimental Set-upOur deployment consisted of 16 wireless nodes (includinglaptops and routers). As previously stated,benchmark users must carefully select the most suitablerouting protocol to provide quality communicationswithout delays nor interruptions. olsrd, developedby the most active and wider communitydevoted to the development of open-source routingprotocols (www.olsr.org), is the most extended implementationof the popular Optimized Link StateRouting (OLSR) protocol. Accordingly, three differentversions of olsrd, using the same configuration,have been selected as benchmarks targets: v.0.4.10(released in 2006), v.0.5.6 (released in 2008) and currentv.0.6.0 (released in 2010).The applicative traffic addressed to exercise thenetwork was defined in terms of synthetic UDP ConstantBit Rate (CBR) data flows of 200 Kbps, similarto the rates observed in daily scenarios [11].In order to recreate some of the most importantproblems in the domain of WMNs [10], we selected asubset of 5 of the most harmful faults (both accidentaland malicious faults or attacks), according to ourprior investigation [4] (see Table I), to be injectedwhile running the workload.TABLE IFaults considered during the experimentationFault Type OriginAmbient noise (A) Accidental NaturalSelective forwarding attack (S) Malicious Human-madeJellyfish attack (J) Malicious Human-madeTampering attack (T) Malicious Human-madeFlooding attack (F) Malicious Human-madeB. Aggregation of MeasuresOnce the benchmark experimental conditions havebeen specified, it is necessary to define the differentmeasures that will be used to assess the quality of theconsidered benchmark target. Conversely to othermeasures-aggregation techniques which are just appliedonce the final measures have been obtained, theLSP technique may assist the benchmark performerto define a comprehensive hierarchical model of measuresapplying a series of refinements. The goal ofthis process is to characterise the quality of the systemthrough a complete and not redundant set ofelemental attributes or variables (a 1 to a n ). This setdefines a block and can contain a different amount ofattributes. This blocks composition continues groupingdifferent characteristics until the global score ofthe system is computed.B.1 Measures SelectionApplying this measures-aggregation strategy in anad hoc network involves characterising this particularsystem through its different characteristics. ForJP2011-375
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GGaliano, V. . . . . . . . . . . .
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Triviño, Francisco . . . . . . . .
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Actas JP2011 - Universidad de La Laguna

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