MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 11; pp. 2794 - 2807
• Main Authors: Tuli, Shreshth, Casale, Giuliano, Jennings, Nicholas R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI for PDC; Artificial intelligence; Artificial neural networks; Cloud computing; Computer networks; Costs; deep learning; Distributed processing; Edge computing; Electronic devices; Energy consumption; Heterogeneity; Machine learning; Mobile computing; monte carlo learning; Optimal scheduling; Optimization; Processor scheduling; Quality of service; Quality of service architectures; Response time; Scheduling; Task analysis; Time factors; workflow scheduling; Workflow software

Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize applicatio...