A Bifactor Approximation Algorithm for Cloudlet Placement in Edge Computing

Bhatta, Dixit; Mashayekhy, Lena

doi:10.1109/tpds.2021.3126256

Cited by 24 publications

(6 citation statements)

References 37 publications

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“…Comparisons were made between meta-heuristic and heuristic algorithms, meta-heuristic and greedy algorithms, reinforcement learning and mathematical programming. In addition, research studies such as [44] have compared metaheuristic and unsupervised learning algorithms, while [65] has compared meta-heuristic and mathematical programming, with results consistently demonstrating the superiority of meta-heuristic algorithms.…”

Section: ) Cloudlet Mobilitymentioning

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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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%

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

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

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“…In addition to the aforementioned studies, recent papers have made significant contributions to the field of edge-server placement. For instance, [27] proposed novel approaches for addressing the challenges in deploying edge servers. Their work focused on optimising the placement of edge servers in order to minimise latency and enhance network performance.…”

Section: Edge-servers Placement Approachmentioning

confidence: 99%

“…Unlike previous approaches, such as refs. [27][28][29], that rely on network size for edge server selection, it offers a more robust and tailored solution. In addition, one significant advantage of this study is the careful selection of sites, ensuring that they are located within one hop from the connected sites.…”

Section: -Scenarios 2 and 3 (20 And 30 Points Respectively)mentioning

confidence: 99%

Optimal intelligent edge‐servers placement in the healthcare field

Jasim

Al-Raweshidy

2023

IET Networks

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The efficiency improvement of healthcare systems is a major national goal across the world. However, delivering scalable and reliable healthcare services to people, while managing costs, is a challenging problem. The most promising methods to address this issue are based on smart healthcare (s‐health) technologies. Furthermore, the combination of edge computing and s‐health can yield additional benefits in terms of delay, bandwidth, power consumption, security, and privacy. However, the strategic placement of edge‐servers is crucial to achieve further cost and latency benefits. This article is divided into two parts: an AI‐based priority mechanism to identify urgent cases, aimed at improving quality of service and quality of experience is proposed. Then, an optimal edge‐servers placement (OESP) algorithm to obtain a cost‐efficient architecture with lower delay and complete coverage is presented. The results demonstrate that the proposed priority mechanism algorithms can reduce the latency for patients depending on their number and level of urgency, prioritising those with the greatest need. In addition, the OESP algorithm successfully selects the best sites to deploy edge‐servers to achieve a cost‐efficient system, with an improvement of more than 80%. In sum, the article introduces an improved healthcare system with commendable performance, enhanced cost‐effectiveness, and lower latency.

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“…The authors reassigned the mobile users to complete the cloudlet placement to balance the workload of each cloudlet, thus minimizing the response time of the system. Bhatta et al [24] formulated the cloudlet placement problem as a multi-objective integer programming model and showed that it is a computationally NP-hard problem. The authors then proposed a bifactor approximate cloudlet placement (ACP) to tackle its intractability.…”

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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A Bifactor Approximation Algorithm for Cloudlet Placement in Edge Computing

Cited by 24 publications

References 37 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

Optimal intelligent edge‐servers placement in the healthcare field

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

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