Dynamic Component Placement and Request Scheduling for IoT Big Data Streaming

Internet-of-Things (IoT) big data streaming applications, such as video surveillance and automatic driving, tend to use mobile-edge computing (MEC) infrastructure to enhance their performance and augment their functionalities. Although extensive previous studies have worked on offloading requests to MEC servers, none of them has comprehensively and thoroughly considered the important features of IoT data streaming applications (i.e., component dependency and dynamic arrival) and the infrastructure provisioning (i.e., capacity constraint and colocation interference). In this article, we consider the offloading problem for dynamically arrived IoT data streaming requests on MEC servers in real time. We model it as a delay-sensitive multiuser multiresource online offloading problem respecting component dependency and capacity constraint. The problem is NP-hard with offloading decisions coupling together. To solve it, we decouple the problem into component placement problem and request scheduling problem and propose a two-stage DPGPD algorithm with polynomial time complexity. We show the first stage dynamic programming (DP) algorithm is the optimal solution and the second-stage greedy primal–dual (GPD) algorithm is asymptotic optimal. The simulation results show that our solution is effective yet efficient compared to benchmark solutions. (DP provides the optimal placement layout with <inline-formula> <tex-math notation="LaTeX">$12 \times $ </tex-math></inline-formula> less decision time of Gurobi; and GPD provides the asymptotic optimal scheduling with <inline-formula> <tex-math notation="LaTeX">$5 \times $ </tex-math></inline-formula> less average waiting time compared to least work left (LWL) in heavy workload.) We implement a dedicated prototype and exploit several representative big data streaming applications to evaluate it. Lab-scale experiment shows that our solution can provide over <inline-formula> <tex-math notation="LaTeX">$3 \times $ </tex-math></inline-formula> less total completion time compared to local execution.