Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

QoS requirements.
Generally speaking, there are five basic dimensions for cloud workflow QoS, viz.
time, cost, fidelity, reliability and security [98]. Time is a basic measurement of
system performance [1, 2, 49]. For workflow systems, the makespan often refers to
the total time overheads required for completing the execution of a workflow. The
total cost often refers to the monetary cost associated with the execution of
workflows including such as the cost for managing workflow systems and usage
charge of cloud resources for processing workflow activities. Fidelity refers to the
measurement related to the quality of the output of workflow execution. Reliability
is related to the number of failures of workflows. Security refers to confidentiality of
the execution of workflow tasks and trustworthiness of resources. Among them,
time, as a basic measurement of performance and general non-functional
requirement, has attracted most of the attention from researchers and practitioners in
the area of such as Software Engineering [84], Parallel and Distributed Computing
[44, 71] and Service Orientated Architectures [35]. In this thesis, we focus on time,
i.e. we investigate the support of high temporal QoS in scientific cloud workflow
systems.
In the real world, most scientific processes are assigned with specific deadlines
in order to achieve their scientific targets on time. For those processes with
deterministic process structures and fully controlled underlying resources, individual
activity durations, i.e. the completion time of each activity, are predictable and
stable. Therefore, process deadlines can normally be satisfied through a build-time
static scheduling process with resource reservation in advance [36, 100]. However,
stochastic processes such as scientific workflows are characterised with dynamic
changing process structures due to the nature of scientific investigation. Furthermore,
with a vast number of data and computation intensive activities, complex workflow
applications are usually deployed on dynamic high performance computing
infrastructures, e.g. cluster, peer-to-peer, grid and cloud computing [4, 65, 92, 95].
Therefore, how to ensure cloud workflow applications to be finished within specific
deadlines is a challenging issue. In fact, this is why temporal QoS is more
emphasised in large scale distributed workflow applications compared with
traditional centralised workflow applications [1].
In the following sections, we will introduce the current work related to temporal
15