MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
This addresses the problem of efficient resource utilization and QoS optimization for users in mobile edge computing, representing an incremental advancement over existing scheduling methods.
The paper tackles workflow scheduling in mobile edge-cloud systems by proposing MCDS, an AI-based method using Monte Carlo learning and deep surrogate models, which improves over state-of-the-art methods by at least 6.13% in energy consumption, 4.56% in response time, 45.09% in SLA violations, and 30.71% in cost.
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 application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties, it is essential, but at the same time challenging, to develop a long-sighted optimization scheme that efficiently models the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network-based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively.