Abstract
This paper proposes a computation offloading scheme for precedence-constrained tasks in a base station-assisted device-to-device (D2D) scenario for the information-centric Internet of Things (IC-IoT). When specified precedence among subtasks cannot be described as simple sequential or parallel relations in a task, the selection of task execution helper for subtasks offloading becomes complex due to the constraints of latency and resources. We define this type of precedence and aim to minimize the time and financial cost of computation task offloading for the user by optimizing subtask-helper pairs. This problem is modeled as a dynamic generalized multi-resource-constrained assignment problem. The optimal offloading policy is offered by searching minimum weight matchings in a bipartite graph. Computer simulations indicate the effectiveness of the proposed approach compared with the random helper selection and priority-based offloading scheme.
Highlights
Ubiquitous connections and heterogeneous devices show new requirements for massive wireless access and complex mobility support in various Internet of things (IoT) scenarios [1] e.g. smart home, intelligent transport system, and smart healthcare
We propose an efficient task offloading scheme based on weighted bipartite graph matching to pair subtasks and helpers
PROBLEM OBJECTIVE AND FORMULATION In this paper, in terms of the given task delay constraint, association constraint and resources constraint, we focus on an efficient task offloading decision to minimize the cost of task process
Summary
Ubiquitous connections and heterogeneous devices show new requirements for massive wireless access and complex mobility support in various Internet of things (IoT) scenarios [1] e.g. smart home, intelligent transport system, and smart healthcare. Applying D2D-MEC into IC-IoT, mobile devices can share the communication and computing resources among each other and the popular task contents. Because of time-varying communication resource volume and computing capability offered by helpers, the transmission and task execution cost is sophisticated and unpredictable. To the knowledge of our best, few works concentrate on computation task offloading problem with this type of precedence relations. The problem formulation aims to minimize the cost of task offloading jointly considering subtasks delay constraints, association states between user and helpers and available resources constraints. We propose an efficient task offloading scheme based on weighted bipartite graph matching to pair subtasks and helpers. By constructing appropriate weight of bipartite graph according to the time-varying system information, we search minimum cost (weight) subtask-helper pairs as the optimal offloading policy.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
