A Heuristic Algorithm Based on Resource Requirements Forecasting for Server Placement in Edge Computing

Xiao, Kaile; Gao, Zhipeng; Wang, Qian; Yang, Yang

doi:10.1109/sec.2018.00043

Cited by 30 publications

(18 citation statements)

References 2 publications

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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 [18], the authors study the actual traffic flow of a city to search for the specific quantity and locations of edge servers with low latency, balanced workload and minimum number of edge servers. The authors in [12] address the challenge of server placement based on resource requirements forecasting and propose a cross-regional resources optimization algorithm with the goal of minimizing the cost of service providers.…”

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%

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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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Section: B Physical Resources Placementmentioning

confidence: 99%

Section: B Physical Resources Placementmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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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“…[16] introduce a cost effective meta-heuristics Cost Effective Genetic Algorithm (CEGA) to minimize total execution cost while meeting the deadline constraint. Xiao et al [17] propose a heuristic algorithm to develop the placement strategy of the edge computing server, the remote cloud and edge servers are combined in a complementary manner to provide seamless task processing based on dynamic conditions of task, network, location and resources.…”

Section: Related Workmentioning

confidence: 99%

An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

et al. 2019

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Edge computing has strong real-time and big data interaction processing requirements. The long scheduling time and load imbalance among edge nodes and edge servers are the key problems of edge computing. The current cloud computing scheduling algorithms all have balance problems between algorithm complexity and performance, and cannot fundamentally solve the contradiction. It is a feasible method to use the deep learning model to train the scheduled data to achieve a direct prediction of the scheduling results. This paper mainly studies from two aspects, one is to obtain more accurate training data from the perspective of researching optimal scheduling algorithms, and the other is to improve the training speed from the perspective of improving the deep learning model. At first, an improved chaotic bat swarm algorithm is put forward. It introduces chaotic factors and second-order oscillation mechanisms to improve the speed of update and dynamic parameter mechanisms. Subsequently, the long short-term memory network deep learning model is trained with the historical data by the improved algorithm. The experimental results show that the improved learning model can achieve the purpose of quickly predicting the scheduling result.INDEX TERMS Edge computing, collaborative scheduling, task scheduling, second-order oscillation mechanisms, improved chaotic bat algorithm, LSTM.

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Model-Based Evaluation and Optimization of Dependability for Edge Computing Systems

Liang

Ali

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Heuristic Algorithm Based on Resource Requirements Forecasting for Server Placement in Edge Computing

Cited by 30 publications

References 2 publications

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

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

An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

Model-Based Evaluation and Optimization of Dependability for Edge Computing Systems

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