@article{GerhardsSanderZivkovicetal.2020,
  author    = {Gerhards, Michael and Sander, Volker and Zivkovic, Miroslav and Belloum, Adam and Bubak, Marian},
  title     = {New approach to allocation planning of many-task workflows on clouds},
  series = {Concurrency and Computation: Practice and Experience},
  volume    = {32},
  journal   = {Concurrency and Computation: Practice and Experience},
  number    = {2 Article e5404},
  publisher = {Wiley},
  address   = {Chichester},
  issn      = {1532-0634},
  doi       = {10.1002/cpe.5404},
  pages     = {1 -- 16},
  year      = {2020},
  abstract  = {Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.},
  language  = {en}
}
@article{TranStaat2020,
  author    = {Tran, Ngoc Trinh and Staat, Manfred},
  title     = {Direct plastic structural design under lognormally distributed strength by chance constrained programming},
  series = {Optimization and Engineering},
  volume    = {21},
  journal   = {Optimization and Engineering},
  number    = {1},
  publisher = {Springer Nature},
  address   = {Cham},
  issn      = {1573-2924},
  doi       = {10.1007/s11081-019-09437-2},
  pages     = {131 -- 157},
  year      = {2020},
  abstract  = {We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.},
  language  = {en}
}