FogFrame: a framework for IoT application execution in the fog

Skarlat, Olena; Schulte, Stefan

doi:10.7717/peerj-cs.588

Cited by 7 publications

(7 citation statements)

References 66 publications

(96 reference statements)

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“…There have recently been a large number of proposals of solutions to the SPP in the Fog or the Cloud [13,31,25,15,12,5,34,40,44], each concentrating on only one of the two environments rather than considering both at the same time [13,5,34]. Few of these solutions have assumed integrated Fog and Cloud computing platforms that have been presented by particular IPs, including Google, Amazon, Microsoft, etc.…”

Section: Motivation and Contextmentioning

confidence: 99%

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A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Azizi

Farzin

Shojafar

et al. 2023

Preprint

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Provisioning services for Internet of Things (IoT) devices leads to severalchallenges: heterogeneity of IoT devices, varying Quality of Services (QoS)requirements, and increasing availability of both Cloud and Fog resources. Thelast of these is most significant to cope with the limitations of Cloud infrastructureproviders (CIPs) for latency-sensitive services. Many Fog infrastructure providers(FIPs) have recently emerged and their number is increasing continually. FLEX isproposed in this work as a platform for selecting a location for service placementin a multi-Fog and multi-Cloud environment. For each service, FLEX broadcastsservice requirements to the resource managers (RMs) of the available Fog andCloud service providers and then selects the most suitable provider for that service.FLEX is scalable and flexible as it leaves it up to the RMs to have their ownpolicy for the placement of submitted services. Service placement and resource selectionhas been formulated as an optimization problem and an efficient heuristicalgorithm is proposed to solve it. Results show that the proposed algorithm canbe used across both Cloud and Fog-based providers.

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Section: Motivation and Contextmentioning

confidence: 99%

“…[25,27]. Moreover, most of them fail to consider the cost and communication delay conditions required and intended by a service provider since there are strict requirements for time-sensitive services [12,15,40,44]. We raise the research questions below to attempt to resolve the above problems.…”

Section: Motivation and Contextmentioning

confidence: 99%

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Azizi

Farzin

Shojafar

et al. 2023

Preprint

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“…If there are insufficient resources to support an IoT application, the fog orchestration control node sends the application to another colony or cloud. As an extension of this work, the authors in [28] propose FogFrame which provides a decentralized approach to manage applications within a Fog landscape.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, many solutions have been proposed for the SPP in Fog and Cloud computing systems [5,10,11,18,23,26,28,32]. However, some of them have focused only on the Fog or Cloud environment and do not consider both together [5,11,26].…”

Section: Introductionmentioning

confidence: 99%

“…There exist some works that are based on the integrated Fog and Cloud computing platform provided by a specific IP such as Amazon, Microsoft and Google [4,18,20]. As time-sensitive services require the low communication latency and computation-intensive services prefer the low monetary cost, a vast majority of the proposed solutions do not take into account the desired delay and cost requirements of service providers [10,28]. To address these issues, we pose the following research questions: i) how can we design and implement an efficient platform to cope with the SPP in joint multi-Fog and multi-Cloud environments?…”

Section: Introductionmentioning

confidence: 99%

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FLEX: A Platform for Scalable Service Placement in Multi-Fog and Multi-Cloud Environments

Farzin

Azizi

Shojafar

et al. 2022

Australasian Computer Science Week 2022

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With the recent development in the Internet of Things (IoT), big data, and machine learning, the number of services has dramatically increased. These services are heterogeneous in terms of the amount of resources and quality of service (QoS) requirements. To cope with the limitations of Cloud infrastructure providers (CIPs) for latency-sensitive services, many Fog infrastructure providers (FIPs) have recently emerged and their numbers are increasing continually. Due to difficulties such as the different requirements of services, location of end-users, and profile cost of IPs, distributing services across multiple FIPs and CIPs has become a fundamental challenge. Motivated by this, a flexible and scalable platform, FLEX, is proposed in this work for the service placement problem (SPP) in multi-Fog and multi-Cloud computing. For each service, FLEX broadcasts the service's requirements to the resource managers (RMs) of all providers and then based on the RMs' responses, it selects the most suitable provider for that service. The proposed platform is flexible and scalable as it leaves it up to the RMs to have their own policy for service placement. The problem is formulated as an optimization problem and an efficient heuristic algorithm is proposed to solve it. Our simulation results show that the proposed algorithm can meet the requirements of services.

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A systematic review on resource provisioning in fog computing

Κaur

Singh

Sharma

2023

Trans Emerging Tel Tech

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Resource provisioning is allocating resources to clients over the Internet and plays a prominent role in cloud computing infrastructure-as-a-service (IaaS).The main issues of resource provisioning are meeting user demands like bandwidth, response time, throughput, availability, and so on. The cloud computing framework uses virtualization technology to provide on-demand access to computer system resources, specifically computation and storage. In cloud computing, computing resources are shared across another communication network using virtualization. However, the cloud computing mechanism cannot meet the needs of a large number of Internet of Things (IoT) services. To consider this, fog computing was introduced as a modified mechanism of the cloud paradigm by providing cloud services for the end devices. Fog computing is a decentralized computing model placed between devices, edges, data centers or the cloud. In this way, it helps to meet the demands initiated by IoT services, such as reduced latency, wide mobility coverage, and so on. Several solutions regarding resource allocation in fog computing are available in the research field, but such solutions have not achieved satisfactory results. Therefore, finding a solution by analyzing recent problems is open research. Therefore, this paper reviews different methods established for resource provisioning in fog computing using different parameters from 2015 to 2021 and introduces the limitations, advantages and future directions related to different resource provisioning techniques.

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FogFrame: a framework for IoT application execution in the fog

Cited by 7 publications

References 66 publications

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

FLEX: A Platform for Scalable Service Placement in Multi-Fog and Multi-Cloud Environments

A systematic review on resource provisioning in fog computing

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