A Decentralized Latency-Aware Task Allocation and Group Formation Approach with Fault Tolerance for IoT Applications
Development of internet of things (IoT) and smart devices eased life by offering numerous applications targeting to provide real-time low latency services, but they also brought challenges in handling huge data generated from the powerful computations, to get a job done. Decentralized edge computing could help to achieve latency requirements of the applications by executing them closer to the user at edge of network, but most of the current studies actually deployed centralized approaches for cluster computing at edge, which put extra overhead of cluster formation and management. In this article, we propose to group heterogeneous edge nodes on task arrival with a more decentralized method and execute tasks in parallel to meet their deadline. On the other hand, to guarantee successful execution of critical IoT application running in an edge network, fault tolerance has to be significantly considered. For resource limited edge devices, there is a great need for efficient fault tolerance techniques, which can provide reliability based on device’s local information, without worrying about overall network topology. In this article, our novel method is to increase task reliability being executed in distributed edge computing environment through finding reliability of an edge node locally, and by providing fault tolerance to increase overall application availability. Our proposed fault tolerance technique works in decentralized mode by executing new algorithms to handle above mentioned problems. Our experiment results show that our approach is effective as well as providing desired goals of achieving deadline for latency-aware IoT applications, with staggering decrease in overall network traffic along with ensuring reliability and availability.
Mudassar, M., Zhai, Y., Liao, L. & Shen, J. (2020). A Decentralized Latency-Aware Task Allocation and Group Formation Approach with Fault Tolerance for IoT Applications. IEEE Access, 8 49212-49223.