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Optimizing customized services: efficient computation in large Service Value Networks

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Abstract

Many times, service innovation occurs when service consumers pose innovative requirements on service providers. The trend towards standardization, simplification and modularization in the service sector has fostered the raise of Service Value Networks where providers and consumers jointly co-create value in an innovative manner. With many different competing services available, the user experience, which is captured by the non-functional Quality-of-Service (QoS) attributes, is an important competitive factor. QoS computation for complex Web services, i.e. the aggregation of QoS factors from atomic services, is essential for an automated an optimized service selection process. However, the computational complexity has often been disregarded in the respective field of research, whereas computational efficiency is inevitable for the application in online scenarios. The threefold contribution of this paper consists of a model for describing the optimization process in Service Value Networks, an extensive elaboration on different optimization techniques that allow for a computational efficient service selection and a broad analytical and simulation-based evaluation of these techniques.

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Notes

  1. Note that the presented implementations do not aim at being most efficient.

  2. As implemented in industry standard solving engines like CPLEX (2012) or Gurobi Optimizer (2013).

  3. Solvers like CPLEX support min and max functions by performing the required transformations (additional variables and constraints) automatically, potentially increasing the BIP formulation’s computational complexity.

  4. Note that the formulation of the approximation functions in the following looks slightly different in a binary integer program, as the aggregation function in such a program must iterate over all decision variables, while multiplying the QoS value with the corresponding decision variable \({{x} \in {\mathcal{X}}}\). For readability reasons, the graph notation is therefore chosen, iterating only over the set \({{\mathcal{A}}}\) representing one feasible service configuration alternative.

  5. For a better readability, parts of the piecewise function above/below the upper/lower boundary are omitted.

  6. A more detailed evaluation can be found in (Haak and Blau 2012).

  7. Note that index l for the l’s attribute of \({{\mathcal{S}}}\) is omitted in this Section.

  8. For evaluating the expected error when coping with stochastic processes, the probability distributions are assumed to be known

  9. Note that normally distributed values for availability can exceed a value of one, which has been neglected in the considerations, but only has a small influence on results. Future work will address this issue by using a beta distribution instead.

  10. The runtime is depicted in a logarithmic scale to achieve a better readability of the graphs.

  11. With δ = 1, no further tuning of the subtractive approximation was performed.

  12. The comparison is based on all simulation runs over all variations of N. Statistical tests are omitted given the effect size and number of total observations.

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Authors and Affiliations

  1. IPE, Research Center for Information Technology (FZI), Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Germany

    Steffen Haak

  2. IISM, Karlsruhe Institute of Technology (KIT), Englerstr. 14, Karlsruhe, Germany

    Christof Weinhardt

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  1. Steffen Haak
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  2. Christof Weinhardt
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Correspondence to Steffen Haak.

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Haak, S., Weinhardt, C. Optimizing customized services: efficient computation in large Service Value Networks. Inf Syst E-Bus Manage 12, 307–335 (2014). https://doi.org/10.1007/s10257-013-0218-z

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