Computing and Information Systems - Theses

Permanent URI for this collection

http://hdl.handle.net/11343/351

Filters

2021 1
1
1
Reset filters

Settings
Statistics
Citations
End date: 2021 × Subject: AI Techniques × Subject: Machine learning ×

Search Results

Now showing 1 - 1 of 1
  • Item
    Thumbnail Image
    Machine Learning-based Energy and Thermal Efficient Resource Management Algorithms for Cloud Data Centres
    Ilager, Shashikant Shankar ( 2021)
    Cloud data centres are the backbone infrastructures of modern digital society and the economy. Data centres have witnessed tremendous growth, consuming enormous energy to power IT equipment and cooling system. It is estimated that the data centres consume 2% of global electricity generated, and the cooling system alone consumes up to 50% of it. Therefore, to save significant energy and provide reliable services, workloads should be managed in both an energy and thermal efficient manner. However, existing heuristics or static rule-based resource management policies often fail to find an optimal solution due to the massive complexity and non-linear characteristics of the data centre and its workloads. In this thesis, we focus on machine learning-based resource management algorithms for energy and thermal efficiency in Cloud data centres which are proven to be efficient in capturing non-linearity between interdependent parameters. We explore how these techniques can be adapted to resource management problems to increase the energy and thermal efficiency of Cloud data centres while simultaneously satisfying application QoS requirements. In particular, we propose algorithms for workload placement, consolidation, application scheduling, and configuring efficient frequencies of resources in Cloud data centres. The proposed solutions are evaluated using various simulation toolkits and prototype systems implemented on real testbeds.