The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

The Infrastructure-as-a-Service (IaaS) cloud is attracting applications due to the scalability, dynamic resource provision, and pay-as-you-go cost model. Scheduling scientific workflow in the IaaS cloud is faced with uncertainties like resource performance variations and unknown failures. A schedule is said to be robust if it is able to absorb some degree of the uncertainties during the workflow execution. In this paper, we propose a novel workflow scheduling algorithm called Dynamic Earliest-Finish-Time (DEFT) in the IaaS cloud improving both makespan and robustness. DEFT is a dynamic scheduling containing a set of list scheduling loops invoked when some tasks complete successfully and release resources. In each loop, unscheduled tasks are ranked, a best virtual machine (VM) with minimum estimated earliest finish time for each task is selected. A task is scheduled only when all its parents complete, and the selected best VM is ready. Intermediate data is sent from the finished task to each of its child and the selected best VM before the child is scheduled. Experiments show that DEFT can produce shorter makespans with larger robustness than existing typical list and dynamic scheduling algorithms in the IaaS cloud.

Cloud computing, a novel distributed paradigm to provide powerful computing capabilities, is usually adopted by developers and researchers to execute complicated IoT applications such as complex workflows. In this scenario, it is fundamentally important to make an effective and efficient workflow application scheduling and execution by fully utilizing the advantages of the cloud (as virtualization and elastic services). However, in the current stage, there is relatively few research for workflow scheduling in cloud environment, where they usually just bring the traditional methods directly into cloud. Without considering the features of cloud, it may raise two kinds of problems: (1) The traditional methods mainly focus on static resource provision, which will cause the waste of resources; (2) They usually ignore the performance fluctuation of virtual machines on the physical machines, therefore it will lead to the estimation error of task execution time. To address these problems, a novel mechanism which can estimate the probability distribution of subtask execution time based on background VM load series over physical machines is proposed. An elastic performance fluctuations-aware stochastic scheduling algorithm is introduced in this paper. The experiments show that our proposed algorithm can outperform the existing algorithms in several metrics and can relieve the influence of performance fluctuations brought by the dynamic nature of cloud.