ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Shen, Chao; Xue, Shengjun; Fu, Shucun

doi:10.1186/s13638-019-1455-8

Cited by 17 publications

(9 citation statements)

References 46 publications

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“…K-means clustering is efficient to cluster the users and associate them to suitable BSs for saving energy [88]- [90]. It can also be applied to the optimization of cloudlet placement [91].…”

Section: A Traditional Ai Algorithmsmentioning

confidence: 99%

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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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Section: A Traditional Ai Algorithmsmentioning

confidence: 99%

“…Shen et al [91] focus on the cloudlet placement to improve energy efficiency in the mobile scenario and K-means clustering [88] method is adopted to search the location center. In this paper, energy consumption is assumed to be directly related to the number of deployed cloudlets.…”

Section: B Energy-efficient Cloud and Edge Computingmentioning

confidence: 99%

AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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“…Liang and Li [43] proposed a location-aware service deployment method based on K-Means for cloudlets which divided MDs into multiple MD clusters according to the geographical location of MDs and then deployed service instances onto the cloudlets closest to the centers of MD clusters. Shen et al [37] proposed a dynamic method for cloudlet placement with clustering algorithm. They used K-Means algorithm to get the initial position of cloudlets.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, researchers have proposed some feasible methods for cloudlet placement in the dynamic environment (to list some here [37], [38]). However, how to minimize the number of cloudlets while serving more MDs, meanwhile, making each cloudlet workload balanced is a very important issue.…”

Section: Introductionmentioning

confidence: 99%

A New Cloudlet Placement Method Based on Affinity Propagation for Cyber-Physical-Social Systems in Wireless Metropolitan Area Networks

Peng¹,

Qian²,

Zhao³

et al. 2020

IEEE Access

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Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved Affinity Propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move. INDEX TERMS Mobile edge computing, cloudlet placement, affinity propagation algorithm, load balancing.

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“…C. Shen et al [36] proposed an energy-efficient cloudlet placement method to reduce the energy consumptions by rearranging the clustering of mobile devices. For a dynamic placement method of energy-efficient cloudlet, the authors have proposed three-step processes such as device center location recognition, cloudlet location determination, and dynamic placement of cloudlet.…”

Section: Big Data On Cloudletmentioning

confidence: 99%

Classification and Big Data Usages for Industrial Applications

Lim¹

2019

IJATCSE

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Recently, Big Data has become a significant concept in various areas, especially with business sectors. Digital devices, social media, and simulation results are producing a vast amount of data which are used by various applications to find insights. However, conceptual work integrating the Big Data applications into categories has been limited. The goal of this paper is to classify Big Data use cases by application's characteristics and to provide a systematic review of Big Data usages in each application classification which is based on examination of the available use cases and survey of related works. Big Data applications are classified into three categories such as industry and business, scientific research, urban planning. The critical aspects of the classified areas are described to determine the importance of Big Data in these areas. Fog computing and cloudlet are presented as the core technology of collecting data. Twitter, LinkedIn, Facebook, and Netflix are discussed as industry and business cases of Big Data.

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ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Cited by 17 publications

References 46 publications

AI based Service Management for 6G Green Communications

AI based Service Management for 6G Green Communications

A New Cloudlet Placement Method Based on Affinity Propagation for Cyber-Physical-Social Systems in Wireless Metropolitan Area Networks

Classification and Big Data Usages for Industrial Applications

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