An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Xu, Xiaolong; Li, Yuancheng; Huang, Tao; Xue, Yuan; Peng, Kai; Qi, Lianyong; Dou, Wanchun

doi:10.1016/j.jnca.2019.02.008

Cited by 148 publications

(88 citation statements)

References 18 publications

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“…It solves the problem of data loss under the condition of transmission delay, which is caused by the uneven requirements of user equipments on resources. Xu et al [59] proposed a computational offloading method named EACO to reduce the energy consumption in smart computing models. Figure 5 shows architecture of smart edge computing, where the shortest path is used to unload tasks.…”

Section: Edge Computingmentioning

confidence: 99%

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Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

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Section: Edge Computingmentioning

confidence: 99%

“…So, more heterogeneous network interfaces can be used by a large amount of services. [59]. Table 3: Work summary of resource allocation and management in fog computing.…”

Section: 25mentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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“…According to Xu et al [96], the set of applications in a Smart City leads to high energy consumption, especially in the mobile devices that interact with them. The authors propose a method called EACO for the reduction of energy consumption in these devices during the execution of the processes.…”

Section: • Methodsmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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“…(17), (18), and (19). The three fitness functions of the computing offloading model (17), (18), (19) represent the time consumption, the energy consumption and the cost, respectively. It is necessary to make tradeoffs among the multiple objective functions.…”

Section: Step 2: Fitness Functions and Constraintsmentioning

confidence: 99%

“…However, compared to a traditional device such as personal computer, a MD has certain limitations in computing power, storage capacity, especially for the battery capacity. Mobile cloud computing (MCC) brings new services and facilities to mobile users (MUs) to take full advantage of cloud computing [15][16][17][18][19][20]. However, the remote cloud is usually located far away from the MUs, which may result in high network latency in the process of data transmission.…”

Section: Introductionmentioning

confidence: 99%

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

Peng

Zhu

Zhang

et al. 2019

J Wireless Com Network

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Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy-and cost-aware for workflow applications in MEC.

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An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Cited by 148 publications

References 18 publications

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

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