Detection Performance of Packet Arrival under Downclocking for Mobile Edge Computing

Wang, Zhimin; Zhao, Qinglin; Xu, Fangxin; Dai, Hong-Ning; Zhang, Yujun

doi:10.1155/2018/9641712

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…With significantly lower overhead, the model-based approaches are able to evaluate the performance of the schemes before their implementations, making them increasingly popular in system design and improvement. Wang et al [21] applied queueing theory to formulate an edge computing system, based on which a near-optimal offloading scheme for the Internet of Vehicles was designed. Ni et al [22] generalized Petri net models and conducted performance evaluation of resource allocation strategies in edge computing environments.…”

Section: Performance Evaluationmentioning

confidence: 99%

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Zhang

Huang

2020

Security and Communication Networks

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Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. With the growing popularity and wide application of GIS in reality, performance has become a critical requirement, especially for mobile GIS services. To attack this challenge, this paper tries to optimize the performance of GIS services by deploying them into edge computing architecture which is an emerging computational model that enables efficient offloading of service requests to edge servers for reducing the communication latency between end-users and GIS servers deployed in the cloud. Stochastic models for describing the dynamics of GIS services with edge computing architecture are presented, and their corresponding quantitative analyses of performance attributes are provided. Furthermore, an optimization problem is formulated for service deployment in such architecture, and a heuristic approach to obtain the near-optimal performance is designed. Simulation experiments based on real-life GIS performance data are conducted to validate the effectiveness of the approach presented in this paper.

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Section: Performance Evaluationmentioning

confidence: 99%

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Zhang

Huang

2020

Security and Communication Networks

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“…[1, ] = 1 (8) else (9) [1, ] = [1, − 1] (10) for ← 2 to n (11) for ← 1 to (12) if (j > d i ) // case (1): the current time step j already exceeds 's deadline (13) then Virtualization technology can be applied on the CPE in many ways, such as by using the VM, container, or VM integration of container. However, a traditional VM consumes many system resources and cannot meet the requirements for light weight and service on-demand deployment.…”

Section: } T I D I J Output: [ ]mentioning

confidence: 99%

“…As the network becomes increasingly flexible, software defined, and virtualized, several standards organizations are working to introduce the SDN/NFV technology into mobile networks to satisfy the low latency requirement of the 5G/IoT network for data processing and information transmission and to indirectly drive the development of the edge computing technology [7][8][9]. Edge computing is a distributed computing architecture that moves computing power for applications, data, and services from a cloud data center server to the CPE that is located close to the edge of the user network to be processed [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling for Edge Computing with Agile VNFs On‐Demand Service Model toward 5G and Beyond

Tseng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.

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“…Due to the emergence of the edge layer, several additional aspects may affect the performance of the system, bringing in more uncertain factors to end-to-end QoS. For instance, task arrivals to the MEC system [19], data processing rates in both edge layer and cloud site [20], and service scheduling strategies [21,22] would all affect the dynamics of the systems, making service selection problem more complex. Therefore, how to design and implement efficient and practical solutions of QoS-aware service selection capturing the dynamics and characteristics of the MEC systems remains largely unexplored.…”

Section: Related Workmentioning

confidence: 99%

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

Huang

Lan

2018

Wireless Communications and Mobile Computing

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Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well-developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS-aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation-based schemes. To attack this challenge, this paper proposes a simulation-based approach of QoS-aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS-aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

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Detection Performance of Packet Arrival under Downclocking for Mobile Edge Computing

Cited by 6 publications

References 13 publications

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Task Scheduling for Edge Computing with Agile VNFs On‐Demand Service Model toward 5G and Beyond

A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing

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