Document Type



Doctor of Philosophy (PhD)


Electrical Engineering

First Advisor's Name

Malek Adjouadi

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

S. Masoud Sadjadi

Second Advisor's Committee Title

Committee Co-Chair

Third Advisor's Name

Armando Barreto

Fourth Advisor's Name

Jean Andrian


distributed systems, scientific computing, medical image processing, job scheduling, virtualization, parallel processing

Date of Defense



This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of “cloud computing” services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves.

In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions:

  • An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments.
  • A performance prediction methodology applicable to the target environment.
  • A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees.

In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia.

Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20-30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.





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