Energy-Efficient Decision Making for Mobile Cloud Offloading

Wu, Huaming; Sun, Yi; Wolter, Katinka

doi:10.1109/tcc.2018.2789446

Cited by 141 publications

(73 citation statements)

References 30 publications

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“…However, (32) shows that the queue length's upper bound rises as V increases. Nevertheless, (7) is satisfied if ε is set as C+V (ê−ě) ϵ . We use O(1/V ) to represent the upper bound of gap of energy consumption, and O(V ) to represent the gap of queue length [21].…”

Section: Algorithm Analysis For Eedoamentioning

confidence: 99%

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Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

Chen

Zhang

et al. 2021

IEEE Trans. Cloud Comput.

236

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With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online way to make the task offloading decisions with polynomial time complexity. Theoretical analysis is given to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiments results are presented which shows the EEDOA's effectiveness.

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Section: Algorithm Analysis For Eedoamentioning

confidence: 99%

“…• Ying Chen, Yongchao Zhang and Xin Chen Computation offloading from the IoT devices to MEC incurs high energy consumption which accounts for a significant portion of the device's total energy consumption [7], [8]. In IoTs, the energy consumption for transmission of each device is greatly affected by the wireless channel state.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

Chen

Zhang

et al. 2021

IEEE Trans. Cloud Comput.

236

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“…There are remarkable research results that are based on reinforcement learning techniques for sequential stochastic decision-making in various computing research domains. For the application of deep reinforcement learning to mobile edge computing, the research contributions in [8][9][10][11] had been discussed about the optimization for their own objective functions. Even if they considered many criteria for the offloading, there are not contributions that aim at the optimal sequential decision-making for offloading decisions, i.e., whether it has to conduct offloading (i.e., centralized computing) or not (i.e., local edge computing).…”

Section: Related Work: Reinforcement Learning For Mobile Edge Computingmentioning

confidence: 99%

“…In mobile edge computing research, several algorithms were proposed in order to optimize their own objectives [8][9][10][11][12]. However, none of them are considering offloading decision for determining whether it has to conduct offloading or not.…”

mentioning

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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“…However, the energy utilization is not satisfied in other regions because of uneven distribution of nodes. To stabilize the energy delay depending on different offloading decision criteria an energy-efficient offloadingdecision algorithm based on Lyapunov optimization is developed in [13]. This approach determines at what time to run the submission locally, when to promote it directly for distant execution to a cloud infrastructure but fails to handle coverage and energy hole problem.…”

Section: Introductionmentioning

confidence: 99%

Alleviation of void and energy hole in mobile cloud offloading using hybrid behavioral intelligence

Preethika¹,

Gobinath²

2018

PEN

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In modern internet the usability of smart mobile devices is rapidly increased. It is not only used for commercial and communication purposes, it can be extended in the field like mobile health care, mobile learning, and mobile games. More access of smart mobile devices has a restriction due to resource constraint, this issue overwhelming via cloud computing. Heavy computations from mobile devices are offloaded to cloud and the application got executed. The problem occurs when the Electrogram data is entered into a mobile network in cloud environment which holds void and energy holes occurrences with heavy traffic and unbalanced load distribution.This paper aims to develop hybrid intelligence behavioural patterns for void and energy hole in mobile cloud offloading. The research work carried out in this paper extends its novelty in stage partitioning for concentric circles and sensor node distribution. The network area distribution of partitioning are uniformly distributed among void hole. The simulation results show the proposed approach produces high energy consumption in the mobile cloud paradigm.

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Energy-Efficient Decision Making for Mobile Cloud Offloading

Cited by 141 publications

References 30 publications

Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

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

Alleviation of void and energy hole in mobile cloud offloading using hybrid behavioral intelligence

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