Distributed Fog‐to‐Cloud computing system: A minority game approach

AbdElhalim, Eman; Obayya, Marwa; Kishk, Sherif

doi:10.1002/cpe.5162

Cited by 8 publications

(6 citation statements)

References 44 publications

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“…AbdElhalim et al (2019) [70] use iFogSim to explore the latency of user nodes offloading tasks in a hyper-dense fog-to-cloud computing network. AbdElhalim et al introduce adaptive low latency networks, irrespective of the number of user nodes, based on game theory.…”

Section: Gupta Et Al (2017)mentioning

confidence: 99%

Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions

et al. 2020

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In recent years, there has been significant advancement in resource management mechanisms for cloud computing infrastructure performance in terms of cost, quality of service (QoS) and energy consumption. The emergence of the Internet of Things has led to the development of infrastructure that extends beyond centralised data centers from the cloud to the edge, the so-called cloud-to-thing continuum (C2T). This infrastructure is characterised by extreme heterogeneity, geographic distribution, and complexity, where the key performance indicators (KPIs) for the traditional model of cloud computing may no longer apply in the same way. Existing resource management mechanisms may not be suitable for such complex environments and therefore require thorough testing, validation and evaluation before even being considered for live system implementation. Similarly, previously discounted resource management proposals may be more relevant and worthy of revisiting. Simulation is a widely used technique in the development and evaluation of resource management mechanisms for cloud computing but is a relatively nascent research area for new C2T computing paradigms such as fog and edge computing. We present a methodical literature analysis of C2T resource management research using simulation software tools to assist researchers in identifying suitable methods, algorithms, and simulation approaches for future research. We analyse 35 research articles from a total collection of 317 journal articles published from January 2009 to March 2019. We present our descriptive and synthetic analysis from a variety of perspectives including resource management, C2T layer, and simulation.

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Section: Gupta Et Al (2017)mentioning

confidence: 99%

Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions

et al. 2020

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“…They are devotedly provisioned for serving data of a particular source. [AbdElhalim et al 2019], [Anzanpour et al 2019], ], [Djemai et al 2019] and [Prabavathy et al 2019] discuss about module-based distributed applications.…”

Section: Distributedmentioning

confidence: 99%

“…These applications aggregate these data and store in Cloud or Fog nodes for future analysis by other applications. [AbdElhalim et al 2019], [Concone et al 2019] and [Karatas and Korpeoglu 2019] mention storage as an application service in Fog computing.…”

Section: Storagementioning

confidence: 99%

“…This orientation assists in vertical scaling of the resources. In [Abbasi et al 2019], [AbdElhalim et al 2019], [Ali et al 2018], [Arkian et al 2017] and , this type of orientation of Fog nodes is highlighted.…”

Section: Hierarchymentioning

confidence: 99%

“…Different meta-heuristic approaches including particle swarm, evolutionary algorithms, game theory and multi-objective optimization are used for making trade-off among various application placement metrics. [AbdElhalim et al 2019], [Fahs and Pierre 2019], [Binh et al 2018], [Fang et al 2019] and [Mishra et al 2018] consider trade-off for placing the applications.…”

Section: Multi-objectivementioning

confidence: 99%

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Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

Mahmud,

Ramamohanarao,

Buyya

2020

Preprint

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The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed, heterogeneous and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

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Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use

Alam

Qazi

Iqbal

et al. 2020

Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications

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Distributed Fog‐to‐Cloud computing system: A minority game approach

Cited by 8 publications

References 44 publications

Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions

Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions

Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use

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