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IADIS International Conference WWW/Internet 2010For ‘Response_Time’ a range for the minimum and maximum deliverable or acceptable value is given.The negotiated range in the final agreement has to be inside this range.4. DECISION MAKING PROCESSThe decision making process proceeds in five steps:1. Collect the constraint sets of consumer and provider. While the consumer gives their set together withthe order, the constraint set of the provider has to be fetched at the SLA Manager.2. Match the constraint lists. As the parameters in the constraint list of the consumer and provider maydiffer, they must be matched to a subset, which represents the intersection of the two lists.3. Match the value sets. Also the given alternatives for a parameter may differ in the provider and theconsumer lists. Especially for continuous values the ranges have to be matched and the preferences for themodified ranges have to be calculated.4. Normalize the priorities and preferences. To ensure that the given priority and preference values arecomparable and fairly used, they have to be normalized.5. Finally the best configuration (proposal) has to be found.To find the best proposal, an optimal solution for a defined objective function Z has to be found.If the parameter values are ranges, i.e. KindOfValue in the meta-information is ‘continuous’, we have tofind an agreement within a common range. As previously described, for ranges the value set consists of onlytwo values. In the third step we have to match the two given ranges (one for every partner), so we have tofind the intersection of the two ranges. Than we have to map the found common range to one preferencevalue for every partner. This is simply done by calculating the mean of the two preferences, which can beobtained from a linear function.With the given priorities and preferences we can calculate the utility function U(P) for a proposal P with nparameters by summarizing the utilities for every parameter:U(P) = ∑ U(v pi ) where U(v pi ) = prio(p i ) * pref(v pi ) and P = { v p1 ,...,v pn }For every partner the best allocation for parameter p i is the value v pi where U(v pi ) has a maximum. Fordetermining the best allocation for a parameter, which maximizes the utility function for both negotiationpartner 1 and 2, we define an objective function Z as:Z(v pi ) = U1(v pi ) + U2(v pi ) - |U1(v pi ) - U2(v pi )| ≡ 2 * min( U1(v pi ), U2(v pi ) )The best allocation v best for a parameter p i is where the objective function Z has a maximum. If we findmore than one allocation, we choose the v as v best , for which the sum U1(v pi ) + U2(v pi ) is highest. If we havestill more than one v we choose one randomly.After finding an allocation for all common parameters, the proposal can be offered to the initiator of thenegotiation.5. CONCLUSIONIn this paper we presented an approach for finding a proposal for a SLA, which fits nearly optimal the needsof the service consumer and the service provider as well. According to Arrows-Theorem [Arrow50] wecannot claim that the found proposal is the best, but it is one of the Pareto-optimal solutions (win-win). Theprovided approach gives the service consumer the possibility to initiate a negotiation independently from theprovided SLA templates. Of course the best fitting values can only be found for parameters which areevaluated by both partners, but the non-matching parameters can be offered to the initiator and be used forfurther (human-based) negotiations as well. We have implemented the decision making process and willevaluate it in collaboration with project partners in the TEXO use case of THESEUS project [Theseus]. Infuture work we have to extend the decision making process to find not only one Pareto-optimal solution butmore or even all to give the partners a choice. While this approach reflects interdependencies between thenegotiable parameters only in building the decision matrix but not in calculating the proposal, we have tobuild up and maximize an objective function Z(P) for a proposal as a whole, instead of maximizing theobjective function for the parameters separately. This will result in a typical constraint satisfaction problem.285