A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

Peng, Kai; Leung, Victor C. M.; Xu, Xiaolong; Zheng, Lixin; Wang, Jiabin; Huang, Qilin

doi:10.1155/2018/8267838

Cited by 85 publications

(51 citation statements)

References 113 publications

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“…Liu et al [23] surveyed systems and tools in EC. Peng et al [24] studied service adaptation and provision in mobile EC (MEC). Mao et al [4] elaborated on the communication perspective of MEC.…”

Section: Dementioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: Dementioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…In addition, extended access capacity at the edge with wireless channels and mobile offloading/ delegation schemes are elevating the probable penetrative and vulnerable vectors in the edge network [6]. The materials available for MEC paradigm covers the areas of communication, offloading, migration, IoT integration, and contrasting with other edge computing paradigms as presented in [1], [3], [7]- [11]. Security is an aspect that has not been addressed specifically in relation to the MEC standardization.…”

Section: A Mec Architecturementioning

confidence: 99%

Realizing Multi-Access Edge Computing Feasibility: Security Perspective

Ranaweera

Jurcut

Liyanage

2019

2019 IEEE Conference on Standards for Communications and Networking (CSCN)

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Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them.

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“…To solve the issue of network latency, a new paradigm called mobile edge computing (MEC) has been proposed [21][22][23]. MEC has become a key technology for realizing the Internet of Things and 5G [24].…”

Section: Introductionmentioning

confidence: 99%

“…while len(P t+1 ) + len(F[ i] ) < N do 15: crowdingdistanceassignment (F[ i] ) by formula 22 16: end while 23: end while 24: return S t ,energy consumption E,time consumption T,cost C constraint are removed from the population, and the new generation is denoted as P t (Line 3 to 8). Two populations P t and O t of size N are randomly generated and form a population R t with a population size of 2N (Line 9 to 10).…”

mentioning

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

Self Cite

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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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A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

Cited by 85 publications

References 113 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Realizing Multi-Access Edge Computing Feasibility: Security Perspective

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

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