QoS-oriented capacity planning for edge computing

Xiao, Yu; Noreikis, Marius; Yla-Jaaiski, Antti

doi:10.1109/icc.2017.7997387

Cited by 30 publications

(37 citation statements)

References 102 publications

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“…Numerical results show very low energy consumption is achieved compared to the baseline which is the optimal case that cannot be realized in practice; hence the distributed approach is used to reduce complexities. The authors of [33] put forth a capacity planning framework that improves the resource utilization of a hierarchical edge cloud network whilst simultaneously meeting QoS requirements in terms of response delay. They do this by taking advantage of diverse demands for CPU, GPU, and network resources.…”

Section: Related Workmentioning

confidence: 99%

“…≤ ℬ ∀ ∈ ℕ (33) Constraints (32) and (33) are used to ensure that, the binary variable ℬ = 1 if network node ∈ ℕ is activated, otherwise ℬ = 0. , = . , ∀ ∈ , ∈ ℙ (34) Constraint (34) ensures that traffic is only directed to the destination node that is hosting a processing service.…”

Section: Intra Server Network Power Consumption ( _ )mentioning

confidence: 99%

“…Fog based solutions have been proposed to improve various performance metrics such as energy, latency, QoS, etc. through various approaches in terms of resource allocation [12], [20]- [25] and architectural design and planning [26]- [33]. The reader is referred to the works in [17] and [34], for architectural design imperatives of fog networks and a detailed taxonomy of fog based solutions, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Distributed Processing for IoT

et al. 2020

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In the near future, the number of Internet connected objects is expected to be between 26-50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this paper, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.

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Section: Related Workmentioning

confidence: 99%

Section: Intra Server Network Power Consumption ( _ )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Distributed Processing for IoT

et al. 2020

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“…Apart from the vehicular traffic, the demand of the fog computing system also depends on the resource consumption of the vehicular applications, which is reflected in the CPU and GPU consumption [8]. The vehicular applications are containerized into Docker Images, and a set of benchmark testing is designed for each containerized application.…”

Section: B Vehicular Application Profilingmentioning

confidence: 99%

“…Capacity planning, which focuses on determining the locations and capacities of fog nodes, is different from the task allocation problem. Noreikis et al proposed a capacity planning solution for edge computing that satisfies the QoS requirements while minimizing the number of required edge computing nodes [8]. However, their framework considers only stationary deployment of fog nodes, and therefore cannot be applied to VFC.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Capacity Planning for Vehicular Fog Computing

Mao¹,

Akgül²,

Mehrabidavoodabadi³

et al. 2021

Preprint

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The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.

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