Journal of Grid Computing

, Volume 15, Issue 4, pp 415–434 | Cite as

Adaptive Resource Allocation with Job Runtime Uncertainty

  • Raul Ramírez-Velarde
  • Andrei TchernykhEmail author
  • Carlos Barba-Jimenez
  • Adán Hirales-Carbajal
  • Juan Nolazco-Flores


In this paper, we address the problem of dynamic resource allocation in presence of job runtime uncertainty. We develop an execution delay model for runtime prediction, and design an adaptive stochastic allocation strategy, named Pareto Fractal Flow Predictor (PFFP). We conduct a comprehensive performance evaluation study of the PFFP strategy on real production traces, and compare it with other well-known non-clairvoyant strategies over two metrics. In order to choose the best strategy, we perform bi-objective analysis according to a degradation methodology. To analyze possible biasing results and negative effects of allowing a small portion of the problem instances with large deviation to dominate the conclusions, we present performance profiles of the strategies. We show that PFFP performs well in different scenarios with a variety of workloads and distributed resources.


Runtime uncertainty Distributed system Resource allocation Self-similarity Heavy-tails 


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentTecnológico de MonterreyMonterreyMéxico
  2. 2.Computer Science DepartmentCICESE Research CenterEnsenadaMéxico
  3. 3.CETYS UniversityTijuanaMexico

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