MOERA: Mobility-Agnostic Online Resource Allocation for Edge Computing

Wang, Lin; Jiao, Lei; Li, Jun; Gedeon, Julien; Mühlhäuser, Max

doi:10.1109/tmc.2018.2867520

Cited by 76 publications

(50 citation statements)

References 38 publications

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“…Ahvar [67] Aït-Salaht [85] Bittencourt [78] Borgia [86] Borylo [71] Boubin [87] Chen [81] De Maio [88] Elgazar [83] Fahs [89] Fricker [72] Gu [80] Habak [20] Liu [77] Liu [50] Mahmud [90] Penner [52] Rodrigues [73] Sardellitti [82] Singh [40] Skarlat [46] Sonmez [91] Tang [79] Tärneberg [75] Wang [45] Wang [70] Wang [92] Wang [93] Xia [94] Yi [76] Zamani [74] 3.4. Objective user.…”

Section: Objective Estimation Discovery Allocation Sharing Optimizationmentioning

confidence: 99%

“…They propose a fair queuingbased task pick-up that ensures a fair execution of the tasks belonging to different services. Wang et al [92] consider placement of user workload with mobile user and include migration as one of the four types of costs included 3. RESOURCE MANAGEMENT AT THE EDGE in the optimization problem, together with operation cost, service quality cost and reconfiguration cost.…”

Section: Multi-perspectivementioning

confidence: 99%

“…Indeed, the arrival times or any other required task information may not be available in advance. For their online solution, Sonmez et al [91] use fuzzy logic for their workload orchestrator while Wang et al [92] propose the MOERA algorithm for solving their formulated mixed nonlinear problem using the regularization technique and convex programming. Also considering online placement, Wang et al [93] propose an approximation algorithm building upon a baseline algorithm giving an optimal solution in the case of a linear application graph.…”

Section: Resource Management At the Edgementioning

confidence: 99%

See 2 more Smart Citations

Latency-aware Resource Management at the Edge

Toczé

2020

Linköping Studies in Science and Technology. Licentiate Thesis

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Section: Objective Estimation Discovery Allocation Sharing Optimizationmentioning

confidence: 99%

Section: Multi-perspectivementioning

confidence: 99%

Section: Resource Management At the Edgementioning

confidence: 99%

See 1 more Smart Citation

Latency-aware Resource Management at the Edge

Toczé

2020

Linköping Studies in Science and Technology. Licentiate Thesis

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“…to promote the economical and sustainable healthcare platform. In [18], [19], [20] and [? ], [21], a healthcare platform for remote patients with better services is designed, but QoS optimization during video transmission through WMMD is not focus of these papers.…”

Section: Introductionmentioning

confidence: 99%

Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Sodhro

Gurtov

Zahid

et al. 2021

IEEE Internet Things J.

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The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First,to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD.

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“…With the rapid development of wireless communications technologies and the wide deployment of mass smart devices, a large number of emerging applications [1], such as artificial intelligence (AI) [2], augmented reality (AR) [3], and virtual reality (VR) [4], have been arising in Internet of Things (IoT) networks, which put forward higher requirements for computation capability and transmit power to the smart device in the IoT network [5]. As we all know, most smart devices usually have limited communication, computation, storage, and energy resources, which is a huge challenge to complete such computation-intensive and delay-sensitive applications [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Fan

Zheng

Jiang

et al. 2020

Sensors

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This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

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MOERA: Mobility-Agnostic Online Resource Allocation for Edge Computing

Cited by 76 publications

References 38 publications

Latency-aware Resource Management at the Edge

Latency-aware Resource Management at the Edge

Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes

Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

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