A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things
By Zhaolong Ning, Peiran Dong, Xiangjie Kong, Feng Xia. IEEE Internet of Things Journal, 2019
425 citations recorded by the source corpus.
With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems
View Paper: https://www.semanticscholar.org/paper/3d4ed02a31238a59f5f944a73404842151f500e1
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In Computer Networks and Communications
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Citations in the corpus
- Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution (2021, background): “The authors in [18] consider the cooperation of a cloud server and several edge servers to execute computation-intensive tasks.”
- Resource Scheduling in Edge Computing: A Survey (2021, background): “Pa rt ia l O ffl oa di ng [79] Revenue a) Task-destination association; b) offloading workload amount determining; c) energy harvesting; [80] Delay, energy consumption a) Computing resource allocation; b) offloading ratio determining; [81] Latency a) Task-destination association; b) offloading ratio”
- Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, neutral): “[79] took the cooperation of MCC and MEC into consideration, where IoT devices can offload their tasks to either MCC or MEC according”