Queuing Model Based Edge Placement for Work Offloading in Mobile Cloud Networks

Chin, Tai-Lin; Chen, Yeong-Sheng; Lyu, Kun-Yu

doi:10.1109/access.2020.2979479

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…The number of content cached at cloudlet increases with the number of offloaded tasks increasing [46]. Content cache performance significantly impacts delay and bandwidth [47]. For example, the popular cached content in cloudlet can reduce the delay and the bandwidth usage of subsequent access to the same content.…”

Section: ) Cache Contentmentioning

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: ) Cache Contentmentioning

confidence: 99%

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

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

View full text Add to dashboard Cite

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“…Most of the works consider the edge server placement as a placement problem by selecting K locations from N candidates with different objections. In this case, generally the edge servers are co-located with access points [8][9][10][11][12][13][14][15][16] or other network functions [17,18]. These works are marked as yes in the column of Co-located.…”

Section: B Physical Resources Placementmentioning

confidence: 99%

“…In addition, server placement and resource allocation are simultaneously considered in some works. In [15], the edge server placement and work allocation strategy is investigated with the goal of minimizing traffic load in the mobile edge networks for green communications. For joint edge server placement and application configuration, a localsearch based algorithm to minimize the service cost and opening cost of edge servers is proposed in [34].…”

Section: B Physical Resources Placementmentioning

confidence: 99%

“…Content may change prior to final publication. [9] no yes yes fixed limited minimized Y. Li et al [10] no no yes fixed limited threshold H. Yin et al [33] no no no unfixed limited minimized T. Lahderanta et al [11] share yes yes fixed limited minimized J. Meng et al [34] share no yes fixed limited minimized Y. Li et al [12] no no no fixed no threshold K. Xiao et al [13] no no yes fixed limited minimized G. Manasvi et al [14] share no yes fixed limited minimized S. Lee et al [17] no yes yes minimized limited threshold T. Chin et al [15] no yes yes fixed no not used X. Xu et al [18] no yes yes fixed limited minimized edge servers, as well as the allocation of server computing resources to provide services towards BSs. The problem is essentially driven by the stochastic BS resource demands, which brings difficulties in practically solving the problem with low complexity.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Among all these factors, the uncertain resource requirements rising from mobile end users (such as users' computing tasks) is the key driven factor for the distribution of edge servers, because the execution resources are deployed to fulfill these requirements. In the literature, a number of studies has dealt with deploying servers in the MEC infrastructure [8][9][10][11][12][13][14][15][16][17][18]. However, these existing studies assume that the resource requirements of end users are deterministic, which largely ignores the pragmatic complexity of the real dynamic world.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Learning Based Framework for MEC Server Planning With Uncertain BSs Demands

et al. 2020

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Mobile Edge Computing (MEC) architecture is composed of geographically distributed edge servers, in which computing capabilities are provisioned at the boundary of the network, which is in close proximity to the end users to provide network services with low latency. The planning of MEC edge servers at appropriate locations is the fundamental first step towards the deployment of the MEC system. In the literature, edge servers planning is based on deterministic resource requirements. This assumption largely neglects the pragmatic complexities imposed by the real dynamic world, in which base station (BS) resource demands are stochastic variables with arbitrary pattern. In view of this fact, we formulate the MEC planning problem as a joint optimization problem of MEC edge servers placement and resource allocation with uncertain BS demands through an uncertain programming formulation. Due to the complexity of this jointuncertain problem, a learning based framework is utilized to practically solve this problem, and the relevance of applying this mechanism in practical usage with sampled arbitrary BS demands data is also discussed. Finally, we conducted intensive real-data driven simulations to evaluate the performance of our proposed mechanism. The results show the effectiveness of our approach with arbitrary BS demands. INDEX TERMS Mobile edge computing, edge server planning, uncertain programming, learning-based framework, resource optimization

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An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

Yadav

Vidyarthi

2023

Concurrency and Computation

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In a hierarchical edge‐fog‐cloud architecture, edge devices possess limited resources and energy. To contain with, it can offload some tasks generated by the Internet of Things (IoT) to the fog and cloud. Several factors influence this task‐offloading decision, including hardware features, network conditions, and application characteristics. Most of the research studies, in task offloading systems, have confined to changing parameter values, whereas very few have considered fuzzy‐based dynamic approaches for resource allocation. This work proposes a fuzzy‐based task offloading technique (FBOT) for scheduling the tasks (both compute and data‐intensive) to the appropriate nodes of the fog‐cloud system. The proposed method incorporates a vague logic‐based fuzzy task scheduler for scheduling various tasks at the fog layer. This helps to reduce the waiting time for the compute‐intensive tasks and minimizes the starvation problem of data‐intensive tasks. In addition, the proposed technique decreases the task completion time and selects the best computing nodes for each tasks. To validate the performance of the proposed technique, extensive simulation studies have been carried out on various parameters. Compared with the baseline algorithms, the results show that the proposed method offers an average improvement of 30% in terms of the total time of the task.

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Queuing Model Based Edge Placement for Work Offloading in Mobile Cloud Networks

Cited by 16 publications

References 20 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 Learning Based Framework for MEC Server Planning With Uncertain BSs Demands

An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture

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