International Journal of Parallel Programming

, Volume 45, Issue 5, pp 1164–1193 | Cite as

An Approach to Forecast Queue Time in Adaptive Scheduling: How to Mediate System Efficiency and Users Satisfaction

  • G. B. Barone
  • V. Boccia
  • D. Bottalico
  • R. Campagna
  • L. CarracciuoloEmail author
  • G. Laccetti
  • M. Lapegna


The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included in distributed environments, appear to be “elastic”, i.e., they are able to shrink or enlarge the number of local physical or virtual resources, also by recruiting them from private/public clouds. This increases the degree of dynamicity, making the infrastructure management more and more complex. Here, we report some advances in the realisation of an adaptive scheduling controller (ASC) which, by interacting with the datacenter resource manager, allows an effective and an efficient usage of resources. In particular, we focus on the mathematical formalisation of the ASC’s kernel that allows to dynamically configure, in a suitable way, the datacenter resources manager. The described formalisation is based on a probabilistic approach that, starting from both a hystorical resources usage and on the actual users request of the datacenter resources, identifies a suitable probability distribution for queue time with the aim to perform a short term forecasting. The case study is the SCoPE datacenter at the University of Naples Federico II.


Adaptive scheduling Resources management Large scale and distributed systems Queue time forecasting 



This work is part of the activities of a multidisciplinary group (GTT), responsible for the SCoPE infrastructure management. It has been realised thanks to the use of the SCoPE computing infrastructure at the University of Naples, also in the framework of PON ”Rete di Calcolo per SuperB e le altre applicazioni” (ReCaS) project.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of Naples Federico IINaplesItaly
  2. 2.Italian National Institute of Nuclear PhysicsNaplesItaly
  3. 3.Italian National Research CouncilNaplesItaly

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