An Edge Computing Matching Framework With Guaranteed Quality of Service

Sharghivand, Nafiseh; Derakhshan, Farnaz; Mashayekhy, Lena; Khanli, Leyli Mohammad

doi:10.1109/tcc.2020.3005539

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…M-QMECS is the modified version of QMECS which can augment social welfare and avoid zero budget surplus compared to QMECS. We then extend our work in [77] considering multiple types of VMs, while preserving all the desired properties such as IC, IR, BB, and AsE. We also improve the computational efficiency of the proposed mechanism by grouping the users with similar QoS requirements in the matching process.…”

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 96%

“…Content may change prior to final publication. and their impact on the cloud/edge resource allocation and pricing [77], [78].…”

Section: Auctions For Market-oriented Cloud/edge Computingmentioning

confidence: 99%

“…This is while sufficient number of Cloudlets should be deployed at the edge in order to satisfy the resource requirements of the users and preserve their quality of experience. In this regard, single or multiple Cloudlets may be assigned to each user depending on her requirements [77], [143].…”

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

“…In [77], [144] we propose two-sided auction mechanisms to match edge resources to IoT devices regarding the QoS requirements of the users and the QoS guarantees of the providers. In [144], we assume that one type of VM is offered by the providers and present two QoS-aware double auction based solutions namely QMECS and M-QMECS.…”

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

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A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

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Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resources management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids-forward, reverse, and two-sided auctions, (2) the heterogeneity of resources-combinatorial auctions, (3) the number and types of adopted attributes in winner determination-multi-attribute auctions, (4) the setting of auction-offline, online, or sequential auctions, and (5) the interoperability among cloud providers-inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented, followed by existing challenges and research directions.INDEX TERMS Auction mechanism design, cloud computing, edge computing, resource management, resource pricing.

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Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 96%

“…Content may change prior to final publication. and their impact on the cloud/edge resource allocation and pricing [77], [78].…”

Section: Auctions For Market-oriented Cloud/edge Computingmentioning

confidence: 99%

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey on Auction Mechanism Design for Cloud/Edge Resource Management and Pricing

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

Self Cite

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“…IoT technology faces challenges such as standardization, cyber security, and energy management [6][7][8]. These problems motivated researchers to examine new IoT architectures for QoS, considering traditional and special attributes such as accuracy, energy consumption, and network optimization.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Software Framework for Improving QoS in IoT

Tomer

Gandhi²

2022

Eng. Technol. Appl. Sci. Res.

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Internet of Things (IoT) and Artificial Intelligence (AI) with its subcomponents are the latest emerging technologies that make our daily lives easier. Quality of Service (QoS) plays a very important role in IoT due to the large number of interconnected nodes. QoS is inversely dependent on the node count, i.e. the increment of nodes causes hampering to QoS, as increasing the number of nodes increases the number of requests to the IoT server. An enhanced framework is strongly needed to control QoS in IoT applications. This study proposes and implements an enhanced framework using Matlab, to control the number of requests. The proposed model can improve QoS parameters like throughput, latency, and packet loss by reducing the number of requests generated by the end nodes without compromising the services to the end user. The results showed that QoS parameters improved in terms of throughput by 5-10%, packet loss by up to 6%, and packet latency by 4%. This model can also be tested in hardware and may provide a better QoS solution.

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