University of Saskatchewan

Associate Professor

Graduate and undergraduate teaching. Research in computer systems and computer networks. Administrative committee work.

University of Saskatchewan

Professor

Teaching, Research, and Administration.

Teaching Operating Systems, Programming Principles and Practices, Computer Systems Organization, Computer Networks, Computer Performance Evaluation, Advanced Parallel and Distributed Systems.

Course coordinator for CMPT 400/405 Honours for many years.

Chair of Academic Programs Committee (College of Arts/Science, Division of Science), University council, Academic Programs Committee (College of Graduate Studies and Research).

Department Curriculum Committee, Department Undergrad Committee, Department Research Seminar Coordinator at times in the past years.

I have been the Graduate Committee chairperson for 2013-2014 and 2014-2015.

In 2015-2016, I was on sabbatical at Google (Mountain View, CA) and DATA61/CSIRO (Sydney, Australia).

Member of University Review Committee (2018-2021).

University of North Carolina

Visiting Researcher

Community PVR (Distributed Media Caching using local Incentives)

DATA61/CSIRO (formerly NICTA)

Visiting Research Scientist

Content Management for Wearable Computers (sensors and distribution)

Lancaster University

Visiting Researcher

Intrusion Detection in Wireless Mesh Networks

Google

Visiting Researcher

Project for Sabbatical - Disk Tail Latency Performance Measurement

MT-WAVE: Profiling Multi-Tier Web Applications

ICPE 2011

MT-WAVE: Profiling Multi-Tier Web Applications

ICPE 2011

Simulation and Performance Evaluation of the Hadoop Capacity Scheduler

IBM CASCON 2014

Hadoop task schedulers like Fair Share and Capacity have been specially designed to share hardware resources among multiple organizations. The Capacity Scheduler provides a complex set of parameters to give fine control over resource allocation of a shared MapReduce cluster. Administrators and users often run into performance problems because they do not know the meaning of different task scheduler parameters and the impact they can have on the running MapReduce workloads across different organizations. The interaction between parameter settings is particularly problematic. In this paper, we implemented a Capacity Scheduler simulator component, integrated it into an existing simulator and then validated the scheduler performance in the simulator with small test cases, consisting of standard benchmark sort programs with different resource requirements. The next step was to study the impact of Capacity Scheduler parameters on different MapReduce workload submission patterns with a more complex set of benchmark programs. Among other results, we found maxCapacity and minUserLimitPCT to be influential parameters as was indicated in our previous work and that using separate queues for short and long jobs provides the best performance in terms of response ratio, execution time and makespan compared to submitting both short and long jobs in the same queue.