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Cloud computing, with elasticity and pay-as-you-go pricing, is a suitable platform for executing workflow applications. Workflow as a Service (WaaS) systems provide scientists with an easy-to-use, and cost-effective platform to execute their workflow applications in the cloud at any time or location worldwide. Quality of Service (QoS) is recognized as a key requirement in WaaS. Monetary cost and time are two primary QoS from a clients’ perspective; whereas, energy consumption is considered a significant problem for cloud providers’ profitability and ability to provide low-cost services. Most workflow scheduling studies assume that workflow tasks have a deterministic Execution Time (ET), which is generally unrealistic in the real world. However, there are few approaches for scheduling in WaaS considering deadlines, and monetary costs with uncertain task ET. These studies typically assume that a cloud resource can execute all types of workflow applications without any need for additional software components. However, using containers is a suitable solution to provide an executable environment for the execution of any workflow type on cloud resources. To this end, we present two cost and energy-aware workflow scheduling that consider the uncertainty in tasks’ ETs. Both solutions are designed for WaaS, leveraging containers to enhance resource utilization rate and reduce energy consumption, resource monetary cost, and workflows deadline violations. Simulated experiments demonstrate that our proposed methods outperform two recent state-of-the-art scheduling algorithms in terms of success rate, monetary cost, energy consumption, and resource utilization rate.

Ensuring reliability—defined as the probability of flawless task execution within a specified duration—for deadline-constrained cloud workflows is challenging due to pervasive uncertainty in task execution time. This uncertainty arises from performance interference on shared infrastructure, leading to random variations in actual task durations. Existing scheduling methods often address task execution time uncertainty or reliability requirements in isolation, failing to satisfy both simultaneously under deadline constraints. To bridge this gap, this paper proposes a novel Reliable Workflow Scheduling under Uncertain task execution Time (RWSUT) algorithm. The core of RWSUT is a dynamic reliability assurance strategy that adaptively decomposes the end-to-end workflow reliability requirement into fine-grained, task-level sub-reliability constraints. These constraints are dynamically adjusted based on the actual reliability achieved by completed tasks, thereby effectively mitigating the cascading impact of execution time uncertainty. Furthermore, an elastic resource provisioning scheme is integrated to dynamically manage the virtual machine pool, which not only satisfies the fluctuating resource demands for reliability but also significantly boosts resource utilization, leading to a substantial reduction in rental costs. Extensive simulations based on real-world scientific workflows demonstrate that RWSUT consistently meets reliability constraint while simultaneously achieving a higher workflow completion rate and lower economic cost compared to state-of-the-art algorithms.