Our paper, titled as "Resource Provisioning in the Edge for IoT Applications with Multi-Level Services", is accepted by IEEE Internet-of-Things Journal for publication.
Abstract: As the prevalence of computing-intensive and delaysensitive IoT applications, IoT Service Providers begin to deploy micro data centers in the edge and offload functions to them. However, more and more complex IoT applications require an ordered sequence of services across geographically distributed infrastructure to fulfil their functions, which poses grand challenges for IoT Service Providers to deploy applications with low costs and high efficiency. To the best of our knowledge, no existing works have studied the deployment for an application with multilevel services (referred to as ADMS problem). To fill in the gap, we formulate the ADMS problem as an optimization problem with the aim of minimizing the overall deployment cost under the latency/computation/storage/bandwidth requirements and the infrastructure capacity limitations. We design a workflow-based heuristic algorithm called AMS, which can determine how many Virtual Machines (VMs) should be placed for each type of service and where to place them. AMS supports the services to scale up or scale down on demand in real time. Simulation experiments based on real network measurement demonstrate that AMS can reduce the number of deployed VMs by 28.4% and the deployment cost by 33.9% subject to comparable satisfied user ratio.
Xu Zhang, Haojun Huang, Hao Yin, Dapeng Wu, Geyong Min, and Zhan Ma, "Resource Provisioning in the Edge for IoT Applications with Multi-Level Services", accepted by IEEE Internet of Things Journal, Oct. 2018.[draft]