  In this paper, we study the market-oriented online bi-objective service
scheduling problem for pleasingly parallel jobs with variable resources in
cloud environments, from the perspective of SaaS (Software-as-as-Service)
providers who provide job-execution services. The main process of scheduling
SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS
providers to schedule end users' jobs and charges users accordingly. This
problem has several particular features, such as the job-oriented end users,
the pleasingly parallel jobs with soft deadline constraints, the online
settings, and the variable numbers of resources. For maximizing both the
revenue and the user satisfaction rate, we design an online algorithm for SaaS
providers to optimally purchase IaaS instances and schedule pleasingly parallel
jobs. The proposed algorithm can achieve competitive objectives in polynomial
run-time. The theoretical analysis and simulations based on real-world Google
job traces as well as synthetic datasets validate the effectiveness and
efficiency of our algorithm.
