Towards Distributed Service Allocation in Fog-to-Cloud (F2C) Scenarios

Souza, Vitor Barbosa; Masip-Bruin, X.; Marín‐Tordera, Eva; Ramírez, Wilson; Sanchez, S.

doi:10.1109/glocom.2016.7842341

Cited by 57 publications

(31 citation statements)

References 3 publications

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“…Mahmud et al also considered a general resource value, but they additionally defined the service demand and device capacity in terms of the expected and offered processing times. On the contrary, this model was sometimes simplified to a scalar value that represents a general capacity unit or with general resources slots . Finally, some other papers defined the hardware resources of the fog computing nodes, but they did not include this constraint in the optimization process to simplify it …”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…Dynamic programming Differently from the others, Souza et al 59 modeled FAPP as a 0-1 multidimensional knapsack problem 80 with the objective of minimizing a given objective function. Limited simulation results on a medium-sized example (40 application components, six fog nodes, and three clouds) are provided by the authors.…”

Section: Deep Learningmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…In addition to offloading user storage and processing tasks to the edge, offloading cloud tasks has been a trend . Souza et al introduced a service distribution strategy among the cloud and fogs, which achieves low delay for service allocation. The work aims at atomizing (ie, breaking down) cloud services into smaller subservices and then executing them on edge devices.…”

Section: Related Workmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

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Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

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“…Based on the collaboration between cloud and fog computing, Alsaffar et al 23 present an architecture of IoT service delegation and resource allocation, in which user requests can be efficiently managed and delegated to appropriate cloud or fog based on linearized decision tree which considers tree conditions (service size, completion time, and VM capacity). Souza and collegues 24,25 address QoS-aware service allocation problem in a combined cloud-fog architecture consisting a duallayer fog aiming to diminish the cloud access delay in IoT scenarios. Meanwhile, Deng et al 7 investigate the tradeoff between power consumption and delay in a cloud-fog computing system.…”

Section: Related Workmentioning

confidence: 99%

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

Pham

Man

Tri

et al. 2017

International Journal of Distributed Sensor Networks

118

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The rapid development of Internet of Things applications, along with the limitations of cloud computing due mainly to the far distance between Internet of Thing devices and cloud-based platform, has promoted a newly distributed computing platform based on collaboration between cloud computing and fog computing. Fog computing helps to reduce transmission latency and monetary cost for cloud resources, while cloud computing helps to fulfill the increasing demands of large-scale compute-intensive offloading applications. In this article, we study the tradeoff issue between the makespan and cloud cost when scheduling large-scale applications in such a platform. We propose a scheduling algorithm called Cost-Makespan aware Scheduling heuristic whose major objective is to achieve the balance between the performance of application execution and the mandatory cost for the use of cloud resources. Additionally, an efficient task reassignment strategy based on the critical path of the directed acyclic graph modeling the applications is also proposed to refine the output schedules of the Cost-Makespan aware Scheduling algorithm to satisfy the user-defined deadline constraints or quality of service of the system. We also verify our proposal by extensive simulations, and the experimental results show that our scheduling approach is more cost-effective and achieves better performance compared to others.

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Towards Distributed Service Allocation in Fog-to-Cloud (F2C) Scenarios

Cited by 57 publications

References 3 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

A collaborative mobile edge computing and user solution for service composition in 5G systems

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

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