Resource Scheduling of CloudIoT Clusters

School of Engineering and Technology
Biplob Ray
Steven Gordon


Resource management activities like resource scheduling and allocation for jobs are crucial for efficient and effective workload management in distributed computing systems. The current proliferating of Cloud-based Internet of Things(IoT) systems are collecting a large amount of data called big data. Many CloudIoT systems require an efficient and speedy process of these data due to time-sensitive nature of the application. Others CloudIoT systems may not be so time-sensitive, hence, can wait for the resources to get free. In this project, you will use publicly available Google, Alibaba, and AWS cloud dataset to model a novel resource scheduling and forecasting algorithm for the cloud cluster computing system. You will –

  • Review of the related literature to define the best methodology to approach and frame the problem
  • Process Google, Alibaba, and AWS using HPC and model their resource scheduling and management techniques.
  • Develop a novel resource scheduling and management technique and middleware for improved performance of CloudIoT paradigm based cloud computing.
Information and Computing Sciences| Engineering| Technology
Distributed computing, Resource scheduling, Internet of Things
October, 2020
Cairns| Melbourne

Project contacts