An Energy and Delay-Efficient Partial Offloading Technique for Fog Computing Architectures

Bozorgchenani, Arash; Tarchi, Daniele; Corazza, G.E.

doi:10.1109/glocom.2017.8254703

Cited by 38 publications

(31 citation statements)

References 9 publications

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“…We first evaluate the performance of the Proposed Offloading Algorithm (POA) against two benchmark algorithms: i) Random Walks Offloading (RWO) [57,58]. ii) Nearest Fog Offloading (NFO) [80,81]. Figure 4 demonstrate the performance based on the average response time to all received service's requests considering different packets type (i.e., heavy-packets and light-packets), however, the random number of heavy or light packets is fixed through out the experiment to ensure consistency in term of load utilization against the offloading algorithms.…”

Section: Resultsmentioning

confidence: 99%

COMITMENT: A Fog Computing Trust Management Approach

Al-Khafajiy

Baker

Asim

et al. 2020

Journal of Parallel and Distributed Computing

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Section: Resultsmentioning

confidence: 99%

COMITMENT: A Fog Computing Trust Management Approach

Al-Khafajiy

Baker

Asim

et al. 2020

Journal of Parallel and Distributed Computing

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“…Considering the fog node is usually battery operated, Arash Bozorgchenani et. al [25] present a suboptimal partial offloading method, which can generate a great impact on the network lifetime according to the simulation results. Moreover, since additional data communication caused by offloading from the cloud to the network edge may incur more overhead, Qiliang Zhu et.…”

Section: B Computation Offloading and Fog Computingmentioning

confidence: 99%

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Ota

Dong

2018

IEEE Trans. Ind. Inf.

395

146

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With the rapid development of Internet of Things (IoT) devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in the paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real-time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network (CNN) model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

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“…In addition, the capacity of edge server is 10,000. This assumption is reasonable because edge servers are generally powerful comparing to user terminal devices, i.e., order of 100 as used in [35]. The capacities of terminal device queue, stanBy Q, and the edge server are set between maximum capacities at the start of the episode.…”

Section: Simulation Settingmentioning

confidence: 99%

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

et al. 2020

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This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance. IntroductionAccording to the fact that the 5G era has been realized based on networking technology innovation, many new computing and communications paradigms are introduced such as millimeter-wave communications, hyper-dense networks, and device-to-device proximal networking [1-3]. Among them, mobile edge computing (MEC) is one of the major technologies for realizing data/computing distribution in order to improve cellular network performance [4]. Based on the benefits of MEC technologies such as data rate improvement and quality enhancement, mobile cellular users can enjoy high quality, seamless, and real-time communication networking services.Together with the MEC technologies, many networked components are also of interest such as cloud servers and connected devices/vehicles. As the number of connected devices/vehicles increases, the amount of data transmitted to the MEC is also rapidly increasing. This obviously introduces serious network limitations such as data processing performance limits, storage capacity limits, and the battery use of terminal devices. Under the consideration of these limitations and problems, the use of cloud computing server is more efficient to deal with big data (gathered from connected vehicles/devices via MEC edges) than the local computing on terminals. On the other hand, for any cases where the data cannot be handled in the cloud servers due to delay requirements and security reasons, MEC devices should be able to handle or process the data from the connected vehicles/devices. Therefore, we can observe the trade-off between cloud computing servers and MEC servers (local edge computing servers). In summary, cloud servers have more power and centralized computing benefits comparing to MEC servers, whereas, it may have limitations when we have delay requirements and security requirements/regulations. Figure 1 shows the combined architecture of local edge computing and cloud computing. As the transmission from connected vehicles/devices (i.e., data upload and download) delay goes down, it is much more suitable for real-time processing. It enables real-time data processing and transmission using high-quality ...

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An Energy and Delay-Efficient Partial Offloading Technique for Fog Computing Architectures

Cited by 38 publications

References 9 publications

COMITMENT: A Fog Computing Trust Management Approach

COMITMENT: A Fog Computing Trust Management Approach

Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

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