Topic 3 - Scheduling and Load Balancing



Global Chair
Andrei Tchernykh
CICESE Centro de Investigación Científica y de Educación Superior de Ensenada, Parallel Computing Laboratory, South Ural State University, Problem-Oriented Cloud Computing Environment International Laboratory, and Russian Academy of Sciences, Institute for System Programming

Local Chair
Sascha Hunold
TU Vienna, Faculty of Informatics, Institute of Computer Engineering, Research Group Parallel Computing

Zhihui Du

Tsinghua University, China

Fanny Dufossé
Inria Grenoble - Rhone-Alpes, France

Alexander Lazarev
Institute for Control Sciences of RAS, Russia

Matthias Mnich
University of Bonn, Germany

Risat Pathan
Zenuity AB, Sweden

Krzysztof Rzadca
Google and University of Warsaw, Poland

Franciszek Seredynski
Cardinal Stefan Wyszyński University in Warsaw, Poland

Bertrand Simon
University of Bremen, Germany

Victor Toporkov
National Research University "Moscow Power Engineering Institute", Russia

Nodari Vakhania
Autonomous University of the State of Morelos, Mexico

Frank Werner
University of Magdeburg, Germany

Prudence Wong
University of Liverpool, UK




New computing systems offer the opportunity to reduce the response times and the energy consumption of the applications by exploiting the levels of parallelism. Heterogeneity and complexity are the main characteristics of modern architectures. Thereby, the optimal exploitation of modern platforms is challenging. Scheduling and load balancing techniques are key instruments to achieve higher performance, lower energy consumption, reduced resource usage, and real-time properties of applications.

This topic invites papers on all aspects related to scheduling and load balancing on parallel and distributed machines, from theoretical foundations for modelling and designing efficient and robust scheduling policies to experimental studies, applications and practical tools and solutions. It applies to multi-/manycore processors, embedded systems, servers, heterogeneous and accelerated systems, HPC clusters as well as distributed systems such as clouds and global computing platforms.


All aspects related to scheduling and load balancing on parallel and distributed machines including but not limited to:

  • Scheduling algorithms for homogeneous and heterogeneous platforms
  • Theoretical foundations of scheduling algorithms
  • Real-time scheduling on parallel and distributed machines
  • Robustness of scheduling algorithms
  • Feedback-based load balancing
  • Multi-objective scheduling
  • Resilient scheduling
  • Scheduling, coordination and overhead at extreme scales
  • On-line scheduling
  • Energy and temperature awareness in scheduling and load balancing
  • Workload characterization and modelling
  • Workflow scheduling
  • Performance models for scheduling and load balancing
  • Management of heterogeneous resources
  • Reproducibility of scheduling


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