Cost-effective replication management and scheduling in edge computing

The high volumes of data are continuously generated from Internet of Things (IoT) sensors in an industrial landscape. Especially, the data-intensive workflows from IoT systems require to be processed in a real-time, reliable and low-cost way. Edge computing can provide a low-latency and cost-effective computing paradigm to deploy workflows. Therefore, data replication management and scheduling for delay-sensitive workflows in edge computing have become challenge research issues. In this work, first, we propose a replication management system which includes dynamic replication creator, a specialized cost-effective scheduler for data placement, a system watcher and some data security tools for collaborative edge and cloud computing systems. And then, considering task dependency, data reliability and sharing, the data scheduling for the workflows is modeled as an integer programming problem. And we present the faster meta-heuristic algorithm to solve it. The experimental results show that our algorithms can achieve much better system performance than comparative traditional strategies, and they can create a suitable number of data copies and search the higher quality replica placement solution while reducing the total data access costs under the deadline constraint.