Adaptive Energy-Aware Computation Offloading for Cloud of Things Systems

Nan, Yucen; Li, Wei; Bao, Wei; Delicato, Flávia C.; Pires, Paulo F.; Dou, Yong; Zomaya, Albert Y.

doi:10.1109/access.2017.2766165

Cited by 92 publications

(57 citation statements)

References 15 publications

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“…Few studies in the reviewed literature have addressed the energy criterion in their system design (e.g. [23,196,294,295,333,384]). Most of the studies on energy are about energy-aware computation offloading, energy-aware mobility management, and federation of IoT devices to improve the energy consumption of fog systems.…”

Section: Mobile Fogmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,030

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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Section: Mobile Fogmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,030

613

View full text Add to dashboard Cite

show abstract

“…To solve the above problem, the researcher proposed an mobile edge computing (MEC) paradigm [12], the mobile edge device (server) is set at the BS, and the computing job of the mobile device can be offloaded on the edge device. The researchers also proposed many similar concepts, such as cloudlet [13], follow-me cloud [14], fog computing [15], small cell cloud [16] and mobile micro-cloud [17].…”

Section: Related Workmentioning

confidence: 99%

“…Although these different concepts produce slightly different implementations, they all recommend placing a small cloud infrastructure at the edge of the network so that users can seamlessly connect to cloud services. In particular, MECs are typically placed only on one or several network hops from mobile users, thus effectively reducing communication delays [12]. Based on MEC model, researchers have done much research work.…”

Section: Related Workmentioning

confidence: 99%

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Wei

Luo

Sun

2020

Sensors

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The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

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“…Assume that there exists satisfying + ∈ Λ, then, under our DSRS algorithm, for any value of the parameter , the time-average queue length defined in (24) is bounded as…”

Section: Theoremmentioning

confidence: 99%

“…Some existing researches have studied the service request scheduling problem in MEC systems. Reference [24] modelled the server in the MEC as one / /1 queue. Reference 2 Wireless Communications and Mobile Computing [2] assumed the offloaded service requests arrived at the MEC system according to a Poisson process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Service Request Scheduling for Mobile Edge Computing Systems

Chen

Zhang

Chen

2018

Wireless Communications and Mobile Computing

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Nowadays, mobile services (applications) running on terminal devices are becoming more and more computation-intensive. Offloading the service requests from terminal devices to cloud computing can be a good solution, but it would put a high burden on the network. Edge computing is an emerging technology to solve this problem, which places servers at the edge of the network. Dynamic scheduling of offloaded service requests in mobile edge computing systems is a key issue. It faces challenges due to the dynamic nature and uncertainty of service request patterns. In this article, we propose a Dynamic Service Request Scheduling (DSRS) algorithm, which makes request scheduling decisions to optimize scheduling cost while providing performance guarantees. The DSRS algorithm can be implemented in an online and distributed way. We present mathematical analysis which shows that the DSRS algorithm can achieve arbitrary tradeoff between scheduling cost and performance. Experiments are also carried out to show the effectiveness of the DSRS algorithm.

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Adaptive Energy-Aware Computation Offloading for Cloud of Things Systems

Cited by 92 publications

References 15 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Dynamic Service Request Scheduling for Mobile Edge Computing Systems

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