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Distributed Data Stream Processing and Task Placement on Edge-cloud Infrastructure

Distributed Data Stream Processing and Task Placement on Edge-cloud Infrastructure

Hot PhD Thesis on Distributed Data Stream Processing and Task Placement on Edge-cloud Infrastructure

Research Area:  Cloud Computing


   Indubitable growth of smart and connected edge devices with substantial processing power has made ubiquitous computing possible. These edge devices either produce streams of information related to the environment in which they are deployed or the devices can be located in proximity to such information producers. Distributed Data Stream Processing is a programming paradigm that is introduced to process these event streams to acquire relevant insights in order to make informed decisions. While deploying data stream processing frameworks on distributed cloud infrastructure has been the convention, for latency critical real-time applications that rely on data streams produced outside the cloud on the edge devices, the communication overhead between the cloud and the edge is detrimental. The privacy concerns surrounding where the data streams are processed is also contributing to the move towards utilization of the edge devices for processing user-specific data. The emergence of Edge Computing has helped to mitigate these challenges by enabling to execute processes on edge devices to utilize their unused potential. Distributed data stream processing that shares edge and cloud computing infrastructure is a nascent field which we believe to have many practical applications in the real world such as federated learning, augmented/virtual reality and healthcare applications.
   In this thesis, we investigate novel modelling techniques and solutions for sharing the workload of distributed data stream processing applications that utilize edge and cloud computing infrastructure. The outcome of this study is a series of research works that emanates from a comprehensive model and a simulation framework developed using this model, which we utilize to develop workload sharing strategies that consider the intrinsic characteristics of data stream processing applications executed on edge and cloud resources.

Name of the Researcher:  Gayashan Niroshana Amarasinghe

Name of the Supervisor(s):  Shanika Karunasekera, Aaron Harwood, Marcos Dias de Assuncao

Year of Completion:  2021

University:  The University Of Melbourne

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