Optimal deployment of mobile cloudlets for mobile applications in edge computing

Jin, Xiaomin; Feng, Gao; Wang, Zhongmin; Chen, Yanping

doi:10.1007/s11227-021-04122-7

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…The cloudlet in the MECE can be static or mobile [12]. In mobile cloudlet networks, it is crucial to figure out how to make load-balancing between all mobile cloudlets so that all resources are utilized and tasks can be processed concurrently and sustainably by different cloudlets, therefore, reducing the average task response time and energy [61].…”

Section: ) Cloudlet Mobilitymentioning

confidence: 99%

“…Offloading is a technique used in the MEC environment to increase the effectiveness of mobile device applications by moving resource-intensive activities to nearby cloudlets [12]. Offloading in MEC mostly refers to running resourceintensive applications on behalf of local mobile devices to minimize workloads, overhead, and processing costs compared to local computing.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

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Mobile Edge Computing (MEC) uses to perform computation operations at the edge of a network for mobile devices. This allows the deployment of more powerful and efficient computing resources in a cost-effective, lightweight and scalable manner. MEC can optimize mobile device performance, enhance security and privacy, improve battery life, provide increased bandwidth, and reduce latency across wireless networks. Cloudlets are a new concept of computations that can perform at the edge of the networks. The service provider can deploy cloudlets services in a MEC environment with the ability for mobile devices to offload their tasks to cloudlets. In the MEC environment, the offloading problem depends on cloudlets' availability of computation resources. Also, the deployment method of cloudlets in the environment will affect the task offloading. This paper investigates the approach to the cloudlet deployment and task offloading problem in the MEC environment. First demonstrate that the problem has to be considered a Multi-objective optimization problem since it needs more than one objective to be optimized. Then prove that the problem is NP-completeness, give an overview of existing solutions using the meta-heuristic algorithms, and suggest future solutions for this problem. Finally, explain the advantages of using Variable-length of solution space with meta-heuristic algorithms for this problem.

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Section: ) Cloudlet Mobilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

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“…The authors then proposed a bifactor approximate cloudlet placement (ACP) to tackle its intractability. In [25], a dynamic cloudlet placement method based on a clustering algorithm (DCDM-CA) was proposed to solve the problem of deploying mobile cloudlets for mobile applications. After determining the placement loca-tion of the cloudlets, the authors also optimized the computational offloading to minimize the system response latency.…”

Section: Related Workmentioning

confidence: 99%

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks

Zhang

Zheng

et al. 2022

Sensors

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With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04–44.99%.

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“…However, they seek an alternative private cloud infrastructure to process the generated data locally and securely before the fine-grained data is sent to the centralised private cloud for other processing that may involve machine learning and artificial intelligence algorithms. This process will provide computing power support for IoT devices when deployed correctly using adequate communication technology such as 5G cellular networks in good coverage [4].…”

Section: Introductionmentioning

confidence: 99%

“…An entropy-weight-based proof of concept algorithm found to be optimal, cost-effective and can meet network delay requirements has been proposed to tackle the cloudlets placement problems [5]. Similarly, a dynamic clustering algorithm-based cloudlets deployment is another approach to solving latency issues in cloudlets due to edge device mobility in smart grids [4].…”

Section: Introductionmentioning

confidence: 99%

Communication Requirements and Deployment Challenges of Cloudlets in Smart Grid

Ugwuanyi

Hansawangkit

Khan

et al. 2022

2022 International Conference on Smart Applications, Communications and Networking (SmartNets)

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Intelligent and distributed power networks are becoming more complex with the addition of cloudlets infrastructure. This paper is the initial output of a Low Latency Edge Containerisation and Virtualisation project at PNDC. This project investigates the performance and communication requirements of deploying edge computing devices to enable low-latency applications. The edge network illustrates how additional intelligent network resources can help realise betterdistributed automation and remote configuration in smart grids. With cloudlets, the performance of smart grid networks will be enhanced in terms of low latency, higher bandwidth, and other network requirements. This paper presents cloudlets technology's current state of the art, including network design requirements, implementation techniques, and integration challenges with the legacy power networks. It also explored the main challenges of processing smart grid data by constrained IoT devices. The feasibility of using edge containers to provide low latency communication to several critical end applications in the smart grid could improve the performance of the networks. Furthermore, the key use cases of cloudlets in critical end applications for power utility networks were identified

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Optimal deployment of mobile cloudlets for mobile applications in edge computing

Cited by 12 publications

References 35 publications

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks

Communication Requirements and Deployment Challenges of Cloudlets in Smart Grid

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