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Intelligent Scaling of Container-based Web Applications in Geographically Distributed Clouds

Research Area:  Cloud Computing


   Cloud data centers are increasingly distributed around the globe. Recently, containerization, a lightweight virtualization technology, has been rapidly adopted as an application packaging mechanism for efficient, consistent web application deployment and scaling within and across Cloud-based data centers. To leverage Cloud elasticity and scalability, containers commonly run on elastic, scalable clusters of virtual machines (VMs). Such global infrastructure and lightweight deployment capabilities offer a perfect choice for deploying latency-sensitive web applications in multiple locations to serve globally distributed users. However, managing container-based web applications, including containers and VMs, in widely dispersed data centers currently lacks intelligent deployment and elasticity capabilities from Cloud providers.
   This thesis investigates several problems related to the lack of such capabilities. This includes problems of deployment such as where and how to deploy VM clusters as well as Geo-replicated application containers across data centers to address potential outages while considering wide-area network latency issues. It also considers how to dynamically deploy clusters across data centers to handle potential spatial workload fluctuations with minimum costs. This in turn gives rise to elasticity problems for multi-cluster container-based web applications deployed to multiple data centers. These problems include how to rapidly scale overloaded clusters at the VM level through temporary inter-cluster resource utilization to avoid Cloud VM provisioning delays. Ideally this should provide sufficient VM resources for the timely launching of new containers in response to sudden workload spikes and avoid costly resource over-provisioning. A further challenge is how to control elastic scaling for both containers and VMs while considering application-level metrics and potential variations in container processing capacity, due to performance interference in shared Cloud data centers. Key to this is the need to optimize performance, availability and costs in a flexible and intelligent manner.

Name of the Researcher:  Yasser Aldwyan

Name of the Supervisor(s):  Richard O. Sinnott

Year of Completion:  2021

University:  The University Of Melbourne

Thesis Link:   Home Page Url