Age-of-Information for Computation-Intensive Messages in Mobile Edge Computing
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| Publicado en: | arXiv.org (Jan 12, 2019), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 2165541913 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2165541913 | ||
| 045 | 0 | |b d20190112 | |
| 100 | 1 | |a Kuang, Qiaobin | |
| 245 | 1 | |a Age-of-Information for Computation-Intensive Messages in Mobile Edge Computing | |
| 260 | |b Cornell University Library, arXiv.org |c Jan 12, 2019 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Age-of-information (AoI) is a novel metric that measures the freshness of information in status update scenarios. It is essential for real-time applications to transmit status update packets to the destination node as timely as possible. However, for some applications, status information embedded in the packets is not revealed until complicated data processing, which is computational expensive and time consuming. As mobile edge server has sufficient computational resource and is placed close to users, mobile edge computing (MEC) is expected to reduce age for computation-intensive messages. In this paper, we study the AoI for computation-intensive data in MEC, and consider two schemes: local computing by user itself and remote computing at MEC server. The two computing models are unified into a two-node tandem queuing model. Zero-wait policy is adopted, i.e., a new message is generated once the previous one leaves the first node. We consider exponentially distributed service time and infinite queue size, and hence, the second node can be seen as a First-Come-First-Served (FCFS) M/M/1 system. Closed-form average AoI is derived for the two computing schemes. The region where remote computing outperforms local computing is characterized. Simulation results show that the remote computing is greatly superior to the local computing when the remote computing rate is large enough, and that there exists an optimal transmission rate so that remote computing is better than local computing for a largest range. | |
| 653 | |a Remote computing | ||
| 653 | |a Packet transmission | ||
| 653 | |a Data processing | ||
| 653 | |a Age | ||
| 653 | |a Messages | ||
| 653 | |a Freshness | ||
| 653 | |a Queuing theory | ||
| 653 | |a Queues | ||
| 653 | |a Computer simulation | ||
| 653 | |a Nodes | ||
| 653 | |a Mobile computing | ||
| 653 | |a Edge computing | ||
| 700 | 1 | |a Gong, Jie | |
| 700 | 1 | |a Chen, Xiang | |
| 700 | 1 | |a Ma, Xiao | |
| 773 | 0 | |t arXiv.org |g (Jan 12, 2019), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2165541913/abstract/embedded/160PP4OP4BJVV2EV?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1901.01854 |