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.
In heterogeneous distributed environment, it is a great challenge to schedule multiple workflows submitted at different times. Particularly, scheduling of concurrent workflows with deadline and budget constraints makes the problem become more complex. Recent studies have proposed dynamic scheduling strategies for concurrent workflows which have limitations in inconsistent environments. Therefore, this paper presents a new dynamic scheduling algorithm for concurrent workflows. This algorithm proposes a uniform ranking that considers the time and costs for both workflows and workgroups to assign priorities for tasks. In the resource selection phase, it controls the resource selection range for each task based on an optimistic budget for the current task and selects resources for the current task according to a defined bi-factor. The experimental results show that our algorithm outperforms the existing algorithms in both consistent and inconsistent environments.  Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end-users under a usage-based payment model. It can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organizations, such as HP Labs in U.S.A., are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in the hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns. Copyright © 2010 John Wiley & Sons, Ltd. Abstract In many domains of science, scientific applications are represented by workflows. In this paper, we introduce a resource management strategy to maximize the success rate of concurrent workflow applications constrained by individual deadline and budget values. The Multi-Workflow Deadline-Budget Scheduling (MW-DBS) algorithm can schedule multiple workflows that can arrive in the system at any time, with the aim of satisfying individual job requirements. MW-DBS produces schedules without performing optimizations but guarantees that the deadline and budget defined for each job are not exceeded. Experimental results show that our strategy increases the scheduling success rate of finding valid solutions.