An Edge Network Orchestrator for Mobile Augmented Reality

Liu, Qiang; Huang, Siqi; Opadere, Johnson; Han, Tao

doi:10.1109/infocom.2018.8486241

Cited by 177 publications

(110 citation statements)

References 21 publications

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“…In the EA framework, the optimizer generates variables vector x to query the fitness function f bbo for obtaining the value of y bbo = f bbo (x). In our scenario, the fitness function is the objective function (T req − T ) 2 + (A req − A) 2 .…”

Section: A Configuration Generationmentioning

confidence: 99%

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FedVision: Federated Video Analytics With Edge Computing

Deng

Ansari

2020

IEEE Open J. Comput. Soc.

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Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices and servers. The challenge of designing FedVision is to optimally use the computing and networking resources for video analytics. Since there is no closed-form expression of the system performance, black-box optimization is employed to optimize the system performance. However, using black-box optimization directly incurs excessive system queries that lead to very poor system performance. To solve this problem, we design a new optimization method that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator. This method allows black-box optimizer to query NPs instead of the real system. We validate the performance of FedVision and the new optimization method using both numerical results and experiments with a testbed. INDEX TERMS Edge computing, black-box optimization, neural process, machine learning, video analytics.

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Section: A Configuration Generationmentioning

confidence: 99%

“…Edge computing coupled with advances in 5G is enabling a plethora of emerging applications, where various smart devices can be connected with each other via the internet and empowered with data analytics [1], [2]. In particular, a large number of smart cameras can be connected to the network and produce a huge volume of video data.…”

Section: Introductionmentioning

confidence: 99%

FedVision: Federated Video Analytics With Edge Computing

Deng

Ansari

2020

IEEE Open J. Comput. Soc.

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“…• Generic edge clouds [15], [16], [19], [21], [29], [30] • Hierarchical clouds [13], [14], [20], [22]- [24], [26] • Fog clouds [18], [25], [27], [28] • Other [17] Decision variables…”

Section: Target Environmentmentioning

confidence: 99%

“…• Workload placement and resource allocation [14], [15], [19], [20], [22], [24], [26], [27], [29] • Workload placement [13], [16], [21], [23], [25], [28] • Other [17], [18], [30] Optimization objective…”

Section: Target Environmentmentioning

confidence: 99%

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QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach

et al. 2020

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Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism. Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-ofthe-art workload distribution method. INDEX TERMS deep reinforcement learning, edge computing, resource allocation, workload distribution

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Leveraging Edge Computing for Mobile Augmented Reality

Lehman

Tan

2020

Fog/Edge Computing for Security, Privacy, and Applications

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An Edge Network Orchestrator for Mobile Augmented Reality

Cited by 177 publications

References 21 publications

FedVision: Federated Video Analytics With Edge Computing

FedVision: Federated Video Analytics With Edge Computing

QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach

Leveraging Edge Computing for Mobile Augmented Reality

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