Specifying the Experimental Scenarios for Simulated Cloud Studies

Simon Bihel

Dept. of Computer Science, ENS Rennnes

Internship done at Irisa in the Myriads team

Advisors: Martin Quinson and Anne-C├ęcile Orgerie


Cloud Structure

Dealing with Fluctuating Workload

Dynamic management: horizontal scaling, vertical scaling, etc.

Typical cloud studies: when to trigger these actions, how to perform them, etc.

Typical Experimental Methodologies

Traditional experiments steps: what are they evaluating, setup, scenario, the results and their analysis.

Simulation has many advantages but real experiments are still more used.


Defining the needs to represent all possible kinds of workloads.

Scientific Needs

  • Discrete workloads representation.
  • Elastic Tasks: Repeating identical microtasks (aka tasks, cloudlet) with fixed size.
  • List of hosts to split workload.
  • Hosts overusage detection.

Technical Needs

  • Output function (for tasks workflows).
  • Real traces of requests (e.g. apache).
  • Detailed platform description (core feature of SimGrid).


Implementation as a SimGrid Plugin.

~400 lines of C++. Hosts overusage detection not fully implemented yet.

Raw Performances

Raw Performances

Real Traces

Tested with the WorldCup 98 data access logs.

One day with ~6 million requests is simulated in ~4 minutes.

The parsing of the trace may be what takes a long time (file size: 43MB).

Specifying the Experimental Scenarios for Simulated Cloud Studies

  • Proposed a description of workloads to approach and ease the process of cloud simulations.
  • Implemented the proposition and showed it was usable.

Further work:

  • Finish implementation of all functionalities.
  • Reproduce papers' experiments.
  • Simulate more complex applications like tasks workflow.