Increasing adoption of scientific workflows in the community has urged for the development of multi-tenant platforms that provides these workflows execution as a service. Workflow as a Service (WaaS) concept has been brought up by researchers to address the future design of Workflow Management Systems (WMS) that can serve a large number of users from a single point of service. This platform differs from a traditional WMS in handling the workload of workflows at runtime. A traditional WMS is usually designed to execute a single workflow in a dedicated process while the WaaS platforms enhance the process by exploiting multiple workflows execution in a resource-sharing environment model. In this paper, we explore a novel resource-sharing policy to improve system utilization and to fulfill various Quality of Service (QoS) requirements from multiple users. We propose an Elastic Budget-constrained resource Provisioning and Scheduling algorithm for Multiple workflows designed for WaaS platforms that is able to reduce the computational overhead by encouraging resource-sharing policy to minimize workflows' makespan while meeting user-defined budget. Our experiments show that the EBPSM algorithm is able to utilize the resource-sharing policy to achieve higher performance in terms of minimizing the makespan compared to the state-of-the-art budget-constraint scheduling algorithm.
Abstract As large-scale distributed systems gain momentum, the scheduling of workflow applications with multiple requirements in such computing platforms has become a crucial area of research. In this paper, we investigate the workflow scheduling problem in large-scale distributed systems, from the Quality of Service (QoS) and data locality perspectives. We present a scheduling approach, considering two models of synchronization for the tasks in a workflow application: (a) communication through the network and (b) communication through temporary files. Specifically, we investigate via simulation the performance of a heterogeneous distributed system, where multiple soft real-time workflow applications arrive dynamically. The applications are scheduled under various tardiness bounds, taking into account the communication cost in the first case study and the I O cost and data locality in the second. The simulation results provide useful insights into the impact of tardiness bound and data locality on the system performance.