Exploring Placement of Heterogeneous Edge Servers for Response Time Minimization in Mobile Edge-Cloud Computing

Cao, Kun; Li, Liying; Cui, Yangguang; Wei, Tongquan; Hu, Shiyan

doi:10.1109/tii.2020.2975897

Cited by 98 publications

(41 citation statements)

References 29 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%

“…To ensure the latency between a server and a BS, the authors in [14] conduct a study of deploying edge servers in heterogeneous edge computing systems to minimize the expected response time while considering the response time fairness of each single BS.…”

Section: B Physical Resources Placementmentioning

confidence: 99%

“…We also identify some studies related to workload sharing. The works in [11,14,34] consider sharing the workload between edge servers, which means that the demands of an access points can be offloaded towards more than one edge servers. It is beneficial to share computing tasks to balance the workloads of edge servers.…”

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: B Physical Resources Placementmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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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Edge server placement for offloading real‐time statistics tasks of bus passenger

Guo

Shang

et al. 2022

Trans Emerging Tel Tech

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With the continuous development of smart bus systems, higher requirements are expected for the accuracy and timeliness of the real‐time statistics of bus passengers. Although the statistical method of image and video processing based on deep learning has higher accuracy, it has higher requirements on the computing power and hardware equipment of the computer. A traditional solution is cloud computing, but cloud computing cannot meet real‐time requirements due to long‐distance transmission. In order to meet the real‐time demand, it can be offloaded to the edge of the network and processed by edge servers. In edge computing, the location of the edge server will have a great impact on the access delay and the traffic load in the edge network. Currently, few people optimize the traffic load in the edge network during the placement process. In view of this, an edge server placement algorithm for task offloading, named ESPTO, is designed to balance the average delay and traffic load under the control of each edge server while minimizing the average delay and traffic load in the edge network. First, a decomposition‐based multi‐objective evolutionary algorithm (MOEA/D) is used to find a better set of placement strategies, and then the optimal placement strategy is obtained through TOPSIS. Experimental results based on the Hangzhou bus station dataset prove the effectiveness of ESPTO.

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Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

et al. 2021

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In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

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Exploring Placement of Heterogeneous Edge Servers for Response Time Minimization in Mobile Edge-Cloud Computing

Cited by 98 publications

References 29 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

Edge server placement for offloading real‐time statistics tasks of bus passenger

Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems

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