Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things
| Autor: | Zhangdui Zhong, Siqi Mu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Edge device
Computer Networks and Communications Computer science Distributed computing Internet of Things Stochastic offloading control lcsh:TK7800-8360 Cloud computing 02 engineering and technology Cooperative computing lcsh:Telecommunication 0203 mechanical engineering lcsh:TK5101-6720 0202 electrical engineering electronic engineering information engineering Computer Science::Networking and Internet Architecture Computation offloading business.industry lcsh:Electronics 020302 automobile design & engineering 020206 networking & telecommunications Energy consumption Computer Science Applications Shared resource Signal Processing Mobile edge computing Enhanced Data Rates for GSM Evolution business Efficient energy use |
| Zdroj: | EURASIP Journal on Wireless Communications and Networking, Vol 2020, Iss 1, Pp 1-21 (2020) |
| ISSN: | 1687-1499 |
| Popis: | With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes. |
| Databáze: | OpenAIRE |
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