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

scale to meet changing user demands quickly; separation of infrastructure
maintenance duties from users; location of infrastructure in areas with lower costs of
real estate and electricity; sharing of peak-load capacity among a large pool of users
and so on. Given the recent popularity of cloud computing, and more importantly
the appealing applicability of cloud computing to the scenario of data and
computation intensive scientific workflow applications, there is an increasing
demand to investigate scientific cloud workflow systems. For example, scientific
cloud workflow systems can support many complex e-science applications such as
climate modelling, earthquake modelling, weather forecast, disaster recovery
simulation, Astrophysics and high energy physics [30, 88, 92]. These scientific
processes can be modelled or redesigned as scientific cloud workflow specifications
(consisting of such as workflow task definitions, process structures and QoS
constraints) at build-time modelling stage [30, 55]. The specifications may contain a
large number of computation and data intensive activities and their non-functional
requirements such as QoS constraints on time and cost [98]. Then, at runtime
execution stage, with the support of cloud workflow execution functionalities such
as workflow scheduling [100], load balancing [12] and temporal verification [19],
cloud workflow instances are executed by employing the supercomputing and data
sharing ability of the underlying cloud computing infrastructures with satisfactory
QoS.
One of the research issues for cloud workflow systems is on how to deliver high
QoS [19, 25]. QoS is of great significance to stakeholders, viz. service users and
providers. On one hand, low QoS may result in dissatisfaction and even investment
loss of service users; on the other hand, low QoS may risk the service providers of
out-of-business since it decreases the loyalty of service users. QoS requirements are
usually specified as quantitative or qualitative QoS constraints in cloud workflow
specifications. Generally speaking, the major workflow QoS constraints include five
dimensions, viz. time, cost, fidelity, reliability and security [98]. Among them, time,
as one of the most general QoS constraints and basic measurements for system
performance, attracts many researchers and practitioners [54, 105]. For example, a
daily weather forecast scientific cloud workflow which deals with the collection and
processing of large volumes of meteorological data has to be finished before the
broadcasting of the weather forecast program every day at, for instance, 6:00pm.
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