On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation

Chen, Qiong; Zheng, Zimu; Hu, Chuang; Wang, Dan; Liu, Fangming

doi:10.1109/tpds.2019.2962435

Cited by 63 publications

(29 citation statements)

References 49 publications

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“…All studies considered the energy and latency objectives during problem formulation and met the applications' Quality of Service requirements. Li et al [13][14][15] and Ying Wah et al [16] suggested energy, latency and cost-aware workload assignments in the distributed mobile edge/fog/cloudlet based cloud network. These studies solved the workload assignment based on NP-Hard scheduling heuristics and meta-heuristics.…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

et al. 2021

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These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.

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Section: Related Workmentioning

confidence: 99%

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

et al. 2021

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“…To develop AI-based green communications, energy consumption of AI algorithms should be analyzed. However, most of the current research just focuses on the network performance improvement compared with conventional algorithms and neglects the consumed energy for the training and running of AI models [277], [278]. This may cause the high complexity of the proposed AI models, which may be more energy-aggressive than traditional methods.…”

Section: F Lightweight Ai Model and Hardware Designmentioning

confidence: 99%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

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“…Deep Transfer Learning as mentioned in section 2.2 is one such area that is useful where the size of datasets is not sufficient [45]. This transfer learning is also useful where computing resources are not sufficient such as Edge or IoT devices [72]. Since edge computing becomes popular in the last few years, so, we restricted this review to the last five years with chronological order.…”

Section: Review Of Related State-of-the-artmentioning

confidence: 99%

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Sufian

Ghosh

Sadiq

et al. 2020

Journal of Systems Architecture

121

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Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.

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On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation

Cited by 63 publications

References 49 publications

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

AI based Service Management for 6G Green Communications

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

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