Data-Aware Task Allocation for Achieving Low Latency in Collaborative Edge Computing
| Autor: | Yuvraj Sahni, Lei Yang, Jiannong Cao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Schedule Edge device Computer Networks and Communications business.industry Computer science Distributed computing 020206 networking & telecommunications Cloud computing 02 engineering and technology Flow network Computer Science Applications Scheduling (computing) Network congestion Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Resource management business Wireless sensor network Edge computing Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 6:3512-3524 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2018.2886757 |
| Popis: | The recent trend in the Internet of Things (IoT) is to distribute and move the computation from centralized cloud devices to edge devices which are closer to data sources. Researchers have proposed collaborative edge computing for IoT where the data and computation tasks are shared among a network of edge devices. One of the important problems in collaborative edge computing is to schedule tasks among edge devices to minimize latency and other performance metrics. Compared to existing works in wireless sensor networks and IoT, there are two additional challenges while scheduling tasks in collaborative edge computing. First, we need to consider the transfer of input data required by different tasks as the data is generated by sensing devices which are located at different geographical places. Second, existing works solve the problem of task scheduling without considering network flow scheduling which can lead to network congestion and long completion times. In this paper, we study the data-aware task allocation problem to jointly schedule task and network flows in collaborative edge computing. We mathematically model the joint problem to minimize the overall completion time of the application. We have proposed a multistage greedy adjustment (MSGA) algorithm where the task scheduling is done by considering both placement of tasks and adjustment of network flows. Performance comparison done using simulation shows that MSGA leads to up to 27% improvement in completion time as compared to benchmark solutions. |
| Databáze: | OpenAIRE |
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