Topic 03: Scheduling and Load Balancing


Scheduling and load balancing techniques are crucial for implementing efficient parallel and distributed applications and for making best use of parallel and distributed systems. These techniques can be provided either at the application level or at the system level, and both approaches are of interest for this topic. At the application level, the mapping of distributed and parallel applications on to infrastructures an the development of dynamic load balancing algorithms that are able to exploit the particular characteristics and the actual utilization of the underlying system are of particular relevance.

At the system level, areas of interest include the support of modern many-core architectures as well as virtual systems like Cloud infrastructures.

The topic area includes workload modeling, and resource management strategies. Theoretical results for designing efficient and robust scheduling strategies are welcome as well as practical application thereof for load-balancing and/or resource management. This applies to HPC parallel computers as well as distributed systems such as clusters, grids, clouds and global computing platforms.


  • Scheduling algorithms for homogeneous or heterogeneous platforms
  • Theoretical foundations of scheduling algorithms
  • Robustness of scheduling algorithms
  • Multi-criteria scheduling
  • Decentralized or hierarchical scheduling
  • Resource management for HPC, Grids and Clouds
  • Evaluation and analysis of load balancing and scheduling techniques
  • Implementations of scheduling and load-balancing algorithms
  • Workload characterization and modeling
  • Workflow and job scheduling
  • Performance models for scheduling and load balancing

Topic Committee

Global chair

Leonel Sousa, INESC-ID/Technical University of Lisbon, Portugal

Local chair

Frédéric Suter, IN2P3 Computing Center, CNRS, France


Rizos Sakellariou, University of Manchester, UK
Oliver Sinnen, University of Auckland, New-Zealand
Alfredo Goldman, University of São Paulo, Brazil