An Innovative Deep-Learning Algorithm for Supporting the Approximate Classification of Workloads in Big Data Environments

  • Alfredo CuzzocreaEmail author
  • Enzo Mumolo
  • Carson K. Leung
  • Giorgio Mario Grasso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


In this paper, we describe AppxDL, an algorithm for approximate classification of workloads of running processes in big data environments via deep learning (deep neural networks). The Deep Neural Network is trained with some workloads which belong to known categories (e.g., compiler, file compressor, etc...). Its purpose is to extract the type of workload from the executions of reference programs, so that a Neural Model of the workloads can be learned. When the learning phase is completed, the Deep Neural Network is available as Neural Model of the known workloads. We describe the AppxDL algorithm and we report and discuss some significant results we have achieved with it.


Workload classification Virtualized environment Deep learning 



This project is partially supported by NSERC (Canada) and University of Manitoba.


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Authors and Affiliations

  1. 1.Universitá della CalabriaRendeItaly
  2. 2.Universitá di TriesteTriesteItaly
  3. 3.University of ManitobaWinnipegCanada
  4. 4.Universitá di MessinaMessinaItaly

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