Privacy-Aware Workflow Scheduling Algorithms in Hybrid Cloud Environments

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 ow scheduling 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 work 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. iii 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.