Objectives

This topic is motivated by the general need for automation and self-managing systems to handle the resource allocations in highly distributed infrastructures serving future Internet-of-things applications. The objective is to develop methods for automatic configuration and optimization of management systems, with the aim to partly or fully automate the configuration and tuning of newly installed management system. This includes tuning of performance-critical parameters that depend on all of infrastructure characteristics, workload dynamics, and application characteristics. The results will contribute to making parts of the Elastisys Cloud Management Platform a self-service product with performance and capabilities of a custom solution.

Expected results

  • Methods and software for automatic tuning of management for different tiers in multi-tier applications and how to correlate management among the tiers.
  • Methods and software to automatically identify performance thresholds, e.g., identifying the optimal compute resource or network load w.r.t. to performance trade-offs with costs or energy consumption.
  • An integrated system for the above functionality, demonstrated in real-world use cases.
  • A management console allowing monitoring and steering of the automatic optimization of an autonomous management system.

 

Location

Elastisys is a spin-off company from the successful cloud resource management research at Umeå University, Sweden. Elastisys provides products and services for scalable and responsive IT systems, with an emphasis on auto-scaling with multi-cloud capabilities. Elastisys products and services extend on decades of internationally leading research in distributed systems, high performance computing, and autonomous management of virtualized resources.

Planned secondments

14 months at Université de Rennes 1; 7 months at Las Naves (Valencia, Spain).

Conditions

Please write your application in English, as it will be examined by multiple people in different European countries. Please do not apply to more than two positions within the FogGuru project.

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