Application-aware cloudlet selection for computation offloading in multi-cloudlet environment

Roy, Deepsubhra Guha; De, Debashis; Mukherjee, Anwesha; Buyya, Rajkumar

doi:10.1007/s11227-016-1872-y

Cited by 81 publications

(41 citation statements)

References 24 publications

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“…In future work, we will focus on multi-objective optimization computation offloading from the perspective of edge servers in MEC. For one thing, the computation offloading for WAs in multi-cloudlet scenario will be studied [49,50]. For another, the revenue of the edge server will be investigated [51].…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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show abstract

“…However, the cloud and the device side are usually very far; the migration between applications and the equipment side always results in a high migration delay. In order to address this challenge, the concept of "cloudlet" is proposed and studied, for example, [17,[45][46][47][48], all describe cloudlet computing and related technologies.…”

Section: Related Workmentioning

confidence: 99%

“…In [47], the author optimizes the relationship between the local cloud and the remote cloud, maintaining a balance between energy consumption and QoS. Roy et al [48] proposed an application-aware selection strategy in the cloudlet scenario, which can promote the energy cost of mobile devices and the communication delay of mobile application.…”

Section: Related Workmentioning

confidence: 99%

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

Shen

Xue

2019

J Wireless Com Network

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The development of mobile cloud computing has greatly improved the computing and storage performance of mobile devices. And mobile cloud computing is undoubtedly the necessary way to solve the performance of the process for mobile applications with high performance requirements. However, migrating the mobile applications to the cloud brings about a migration delay, which is intolerable for high real-time demanding applications. This can be technically achieved by expanding mobile cloudlets, co-located with access points (AP). Then, how to deploy the cloudlet to reduce the energy consumption has become a part of major challenges in the current study. In this paper, we propose an energy-efficient cloudlet placement method, named ECPM, to effectively reduce the number of cloudlets, so as to achieve the energy savings. Specifically, the clustering of mobile devices at each time is obtained, and then the cloudlet will move to the clustering to achieve the best use of energy. Finally, the experimental results demonstrate that the proposed method is energy saving.

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“…There are many studies on single MUCO. The more representative ones are as follows ( [51][52][53][54][55][56][57][58]). We Cloudlet Based Single MUCO.…”

Section: Computation Offloading Computation Offloading Wasmentioning

confidence: 99%

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

Peng

Leung

et al. 2018

Wireless Communications and Mobile Computing

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Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

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Application-aware cloudlet selection for computation offloading in multi-cloudlet environment

Cited by 81 publications

References 24 publications

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

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

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

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

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