|dc.contributor.author||Sharif Nabavi, Shaghayeh||-|
|dc.description.abstract||Hybrid clouds have gained popularity with many organisations in recent times due to
their ability to provide additional computing capacity to private clouds. A hybrid cloud
complements private cloud computing resources when there is resource scarcity. However,
deploying distributed applications, such as work
ows, in hybrid clouds introduces
new challenges such as the privacy of tasks and data in scheduling work
ows. The key
problem is the danger of exposing private data and tasks in a third-party public cloud
infrastructure, especially in healthcare applications. In this thesis, we tackle the problem
of scheduling work
ows in hybrid clouds while considering the multi-level privacy and
deadline constraints. This research is di erent from most studies on work
in which the main goal is to achieve a balance between desirable yet incompatible
constraints. Although many others have addressed the trade-o between cost and time,
or between single-level privacy and cost, their work still su ers from insu cient consideration
of the trade-o between multi-level privacy constraints and time.
To address such shortcomings in the literature, we introduced a new privacy-preserving
method to execute work
ows with multi-level privacy of tasks and data. This privacypreserving
method assists private cloud providers and work
ow owners to deploy their
ows in the hybrid cloud environment without the concern of exposing sensitive
information in the public cloud environment.
We proposed three static (o -line) scheduling algorithms to minimise the execution
cost of a single work
ow in hybrid clouds while meeting the privacy and deadline constraints.
The evaluations of these algorithms indicate their e ciency in minimising the
scheduling cost in time-pressured scenarios. We further proposed two formulations using
mixed integer linear programming (MILP), namely, the discrete-time model and
the continuous-time model, to schedule single work
ows in a hybrid cloud environment
while satisfying deadline and privacy constraints. We investigated the advantages and
disadvantages of using these models and their impacts on the scheduling cost, as well as
their solving time. We observed that using MILP to formulate and solve the scheduling
problem would produce satisfactory results in reducing the work
ow execution cost in
hybrid clouds. We improved the continuous-time model by applying a greedy heuristic.
This resulted in a faster solving time at the price of a higher scheduling cost.
In addition, we introduced two dynamic (on-line) multiple work
ow scheduling algorithms.
These algorithms considered the dynamic nature of the hybrid cloud environment
where the availability of cloud resources can be increased or decreased at any
point of the scheduling time.
In a nutshell, we considered both static and dynamic con gurations for hybrid clouds
in developing the scheduling algorithms. We evaluated the scheduling algorithms (online
and o -line) using real-world work
ow datasets. The results show that the proposed
scheduling algorithms are e cient in reducing the cost of executing work
ows in hybrid
clouds under multi-level privacy and deadline constraints in time-pressured scenarios.||en_AU|
|dc.publisher||University of Sydney||en_AU|
|dc.publisher||Faculty of Engineering & Information Technologies||en_AU|
|dc.rights||The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.||en_AU|
|dc.title||Privacy-Aware Workflow Scheduling Algorithms in Hybrid Cloud Environments||en_AU|
|dc.type.pubtype||Doctor of Philosophy Ph.D.||en_AU|
|dc.description.disclaimer||Access is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.||en_AU|
|Appears in Collections:||Sydney Digital Theses (University of Sydney Access only)|