Resource scheduling of workflow multi-instance migration based on the shuffled leapfrog algorithm

Purpose: When the workflow changed, resource scheduling optimization in the process of the current running instance migration has become a hot issue in current workflow flexible research; purpose of the article is to investigate the resource scheduling problem of workflow multi-instance migration. Design/methodology/approach: The time and cost relationships between activities and resources in workflow instance migration process are analyzed and a resource scheduling optimization model in the process of workflow instance migration is set up; Research is performed on resource scheduling optimization in workflow multi-instance migration, leapfrog algorithm is adopted to obtain the optimal resource scheduling scheme. An example is given to verify the validity of the model and the algorithm. Findings: Under the constraints of resource cost and quantity, an optimal resource scheduling scheme for workflow migration is found, ensuring a minimal running time and optimal cost. Originality/value: A mathematical model for resource scheduling of workflow multi-instance migration is built and the shuffled leapfrog algorithm is designed to solve the model.Peer Reviewe

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Automated Diagnosis of Clinic Workflows

Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization