Winning at the Starting Line: Joint Network Selection and Service Placement for Mobile Edge Computing

Gao, Bin; Zhou, Zhi; Liu, Fangming; Xu, Fei

doi:10.1109/infocom.2019.8737543

Cited by 163 publications

(62 citation statements)

References 21 publications

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“…The other recognizes criticality of the network in service performance, and employs active network planning to best utilize network information and/or allocate network resource. For instance, Gao et al [12] explored how access networks selection can improve service QoS. Josilo et al [13] and Liu et al [19,20] studied multi-dimensional resource allocation including both computing and network resources.…”

Section: Performance Evaluation 51 Evaluation Methodsmentioning

confidence: 99%

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Robust resource provisioning in time-varying edge networks

Xue

Wan

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and uctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these dierent factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several eciency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

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Section: Performance Evaluation 51 Evaluation Methodsmentioning

confidence: 99%

“…where (D t r (t)) + = max{D t r (t), 0}, and R is the real number set. Note that in (12), the VaR is automatically computed as the minimizing value of r (t). Merging (12) into Program (11), we have…”

Section: Cvar-based Formulationmentioning

confidence: 99%

Robust resource provisioning in time-varying edge networks

Xue

Wan

et al. 2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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show abstract

“…Although the authors considered heterogeneous edge resources, the computation model was not dynamic. The authors of [37] addressed both the problems of network selection and service placement for MEC infrastructure. Towards the reduction of the complexity of the general problem, they decomposed it into a series of sub-problems and solved them in an iterative fashion.…”

Section: Performance Modeling and Resource Allocation In Cloud And Edmentioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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“…Because the problem of network selection and service placement is NP-hard [31], this paper uses PSO algorithm to solve it. This algorithm is good at NP-hard problem optimization [32].…”

Section: Ivalgorithm Descriptionmentioning

confidence: 99%

Joint Network Selection and Service Placement Based on Particle Swarm Optimization for Multi-Access Edge Computing

Song

Zhao

et al. 2020

IEEE Access

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With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This paper studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC's access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this paper designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this paper designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost. INDEX TERMS Multi-access edge computing, particle swarm optimization, quality-of-service.

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Winning at the Starting Line: Joint Network Selection and Service Placement for Mobile Edge Computing

Cited by 163 publications

References 21 publications

Robust resource provisioning in time-varying edge networks

Robust resource provisioning in time-varying edge networks

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Joint Network Selection and Service Placement Based on Particle Swarm Optimization for Multi-Access Edge Computing

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