QoS-Aware Matching of Edge Computing Services to Internet of Things

Sharghivand, Nafiseh; Derakhshan, Farnaz; Mashayekhy, Lena

doi:10.1109/pccc.2018.8711148

Cited by 15 publications

(9 citation statements)

References 29 publications

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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. Our proposed auctions not only provide efficient resource allocation solutions to maximize obtained social welfare, they also ensure that the matching providers will be able to provide the required QoS by users.…”

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: 99%

Section: Auction-based Edge Resource Management and Pricingmentioning

confidence: 99%

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

Sharghivand¹,

Derakhshan

Siasi

2021

IEEE Access

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“…b) Stream processing time reduction: Sharghivand et al [9] proposed a two-sided matching model for allocating Fog resources to services at the edge of network considering the service response time. The approach improves the user satisfaction and quality of experience using a set of heterogeneous quality of service metrics.…”

Section: Introductionmentioning

confidence: 99%

A Two-Sided Matching Model for Data Stream Processing in the Cloud – Fog Continuum

Mehran

Kimovski

Prodan

2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Latency-sensitive and bandwidth-intensive stream processing applications are dominant traffic generators over the Internet network. A stream consists of a continuous sequence of data elements, which require processing in nearly real-time. To improve communication latency and reduce the network congestion, Fog computing complements the Cloud services by moving the computation towards the edge of the network. Unfortunately, the heterogeneity of the new Cloud -Fog continuum raises important challenges related to deploying and executing data stream applications. We explore in this work a two-sided stable matching model called Cloud -Fog to data stream application matching (CODA) for deploying a distributed application represented as a workflow of stream processing microservices on heterogeneous computing continuum resources. In CODA, the application microservices rank the continuum resources based on their microservice stream processing time, while resources rank the stream processing microservices based on their residual bandwidth. A stable many-to-one matching algorithm assigns microservices to resources based on their mutual preferences, aiming to optimize the complete stream processing time on the application side, and the total streaming traffic on the resource side. We evaluate the CODA algorithm using simulated and real-world Cloud -Fog experimental scenarios. We achieved 11-45% lower stream processing time and 1.3-20% lower streaming traffic compared to related state-of-the-art approaches.

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“…These resources-rich components placed closer to the users are called Cloudlets [5]. Cloudlets enable task offloading for resource-intensive mobile applications to provide realtime and location-aware services, while reducing traffic to the conventional cloud [3,7,8]. Strategically placing these geo-distributed cloudlets to provide low-latency computing services is a major challenge in edge computing.…”

Section: Introductionmentioning

confidence: 99%

Cost-aware cloudlet placement in edge computing systems

Bhatta

Mashayekhy

2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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One of the well-known challenges in Edge Computing is strategic placement of cloudlets. The fundamental goals of this challenge are to minimize the deployment cost and to guarantee minimum latency for the users of edge services. We address this challenge by designing a cost-aware cloudlet placement approach that fully maps user applications to appropriate cloudlets while ensuring their latency requirements. We investigate the effectiveness of our proposed approach by performing extensive experiments based on New York City OpenData. The results show that our approach obtains close to optimal cost solutions with significantly reduced execution time.

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QoS-Aware Matching of Edge Computing Services to Internet of Things

Cited by 15 publications

References 29 publications

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

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

A Two-Sided Matching Model for Data Stream Processing in the Cloud – Fog Continuum

Cost-aware cloudlet placement in edge computing systems

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