Introducing: Dr Mohan Baruwal Chhetri, Research Fellow, Internet of Things and Software Systems Lab


Briefly tell us about your research.

My research broadly centres around the adaptive, automated management of open, large-scale, distributed service-oriented systems with a particular focus on quality of Service (QoS) and Service Level Agreement (SLA) management in the area of cloud and services computing. Key research projects include:

  • Smart Cloud Broker – Smart Cloud Broker ( is a suite of software tools that allows cloud infrastructure consumers to (a) compare the different Infrastructure as a Service (IaaS) offerings from various cloud service providers, and (b) purchase computing resources with the most appropriate specification that best meets their technical and business requirements from the most competitive provider. Each component of the Smart Cloud Broker provides a unique functionality that can be used individually or collectively.
    • Smart CloudBench is a tool for the automated performance benchmarking of public and private infrastructure as a service (IaaS) offerings.
    • Smart CloudMonitor is a tool for the real-time, cross-layer performance monitoring of cloud service-based applications.
    • Smart CloudPurchaser is a policy-driven tool for the automated procurement and management of computing resources from public IaaS providers based upon dynamic user requirements and constraints.

The Smart Services CRC, Commercialisation Australia, AARNeT and Zimbani are the main collaborators on this project.

  • Consumer-centric Adaptive Quality-assured Cloud Services Brokerage – This is a collaborative R&D project supported by the industry partner Zimbani and the ARC Linkage program. We are currently developing an innovative framework with new mechanisms and software tools for quality-assured cloud service brokerage. The Smart Cloud Manager will provide an add-value intermediary service for managing quality-assured provision of cloud services based applications through adaptive handling of QoS requirement changes on the consumer side and SLA violation as well as changed QoS offerings on the provider side.
  • Spot Bid Estimator – This is a tool for estimating Bid prices for computing resources on the Amazon EC2 Spot Market based on the computing needs of consumers and the minimum sustained availability duration. It uses the historical pricing information and time series forecasting models to predict future Spot prices, and then estimates the most optimal Bid based on the forecast prices.

My projects are industry driven and collaborators include AARNET, Suncorp, Segura and Zimbani. Some key highlights include:

  • Trialling of Smart CloudBench at a number of Australian Universities in collaboration with AARNET (2015)
  • Best Paper award at the Third Australasian Symposium on Service Research and Innovation (2013)
  • Pitch Presentation at the $50,000 I.T Invention Test (Geelong) in 2012

More details are available online  at, and

Who are your research colleagues and/or supervisors?

My supervisors are Prof. Ryszard Kowalczyk and Assoc. Prof. Quoc Bao Vo. I also collaborate closely with Dr. Markus Lumpe at Swinburne University and Dr. Surya Nepal from Data61.

What research equipment are you using (if any)?

Since I do a lot of cloud-related research, I make extensive use of cloud infrastructure including Nectar (, Amazon EC2, Google Compute Engine and Microsoft Azure. Most of the software tools that I use are open-source including Telegraf, InfluxDB and Kapacitor from InfluxData (, Grafana (, RStudio ( and Shiny (

Did you work across locations?

Yes and No. I am physically based at the Hawthorn campus but I work virtually from several different locations since I do most of my work on the cloud. For example, the Nectar cloud has data centers in Victoria, UNSW, SA, WA, Tasmania and Queensland and at any given time, I have multiple virtual machines running across these data centers.

What is the one thing you wish you had known at the beginning of your research that you know now?

As a researcher, you want to build a perfect solution that can take up a lot of your time but once finished the solution is either outdated or overtaken by other solutions. Given the rapid rate at which technology is changing, the old style approach to research and innovation is no long applicable and one has to use the fail fast, fail often approach. This requires a mental shift in one’s approach to R&D&I.