Deploying Fog Applications: How Much Does It Cost, By the Way?

Brogi, Antonio; Forti, Stefano; Ibrahim, Ahmad

doi:10.5220/0006676100680077

Cited by 32 publications

(25 citation statements)

References 21 publications

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“…The devised approach works on arbitrary application and infrastructure graph topologies. Later on, Brogi et al extended their initial model and algorithms probabilistically to estimate QoS assurance, resource consumption in the fog layer, and monthly deployment cost of eligible placements. QoS‐assurance is estimated by means of Monte Carlo simulations so to consider variations in the QoS of end‐to‐end communication links and to predict how likely each application placement is to comply with the desired network QoS .…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…On the contrary, link reliability is seldom considered in the analyzed research works. Various works solely considered latency among the metrics related with the network, often but not always along with bandwidth . Bandwidth was also considered by Taneja and Davy and Arkian et al along with hardware constraints.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…Various works solely considered latency among the metrics related with the network, 26,42,49,56,57,59 often but not always along with bandwidth. 24,[28][29][30][31][32]43,58 Bandwidth was also considered by Taneja and Davy 36 and Arkian et al 38 along with hardware constraints. On the contrary, the studies that included link reliability also included other network constraints, such as bandwidth, 34,55 latency, and topology, 53 or latency, bandwidth, and topology.…”

Section: Network Constraintsmentioning

confidence: 99%

“…The resource capacity is usually modelled as a vector (or set) of elements, one for each of the hardware elements considered in the fog devices or the network. Examples of those vectors for the case of the fog devices are: considering CPU 24,58 ; considering CPU and storage 38 ; CPU, RAM, and storage 30,31,40,47 ; considering only storage. 39,41,45 Examples of resource capacity vectors both for fog nodes and network are: considering CPU, RAM, and network bandwidth 36,55 ; considering CPU, RAM, storage, and network bandwidth 33,50 ; considering processing and transmission capacities.…”

Section: Node Constraintsmentioning

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%

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

Section: Network Constraintsmentioning

confidence: 99%

Section: Node Constraintsmentioning

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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“…The cost is calculated based on Equation (). The unit costs for the execution environment considered in our experiments is given in Table 5 32 Reliability: We consider a reliable execution of the application to ensure that atleast all the mandatory components of the application are up and running for a period of time.…”

Section: Experimental Design and Setupmentioning

confidence: 99%

CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

2020

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Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability‐aware Eagle‐Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi‐Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta‐heuristics such as NSGA‐II and MOWOA based placement strategies.

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An adaptive and cost‐efficient migration to cloud approach in dynamic environments

Bushehrian

Nabavi

2021

Concurrency and Computation

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Summary Due to the variety in available cloud providers along with the frequent changes in strategic objectives of an enterprise, migrating existing software components to the cloud has become a challenging decision in the software maintenance phase. “Financial” and “customer satisfaction” viewpoints are two important strategic objectives of all enterprises that greatly affect the decision about migration to the cloud. Moreover immense number of target cloud services with too many configurations and cost models has made the search space of possible migrations huge. Many existing approaches of software migration to the cloud have modeled the problem as deployment optimization of software components over available platforms, while in this research following a valid migration plan is intended rather than proposing a final optimal migration solution (deployment). A migration plan is a sequence of actions to be taken by the technical team to move the software components to the cloud stepwise. Since at each stage of the migration there might be many valid alternative paths to follow, a recommender module is proposed to direct the management by recommending the best migration plan out of all valid plans in a Labeled Transition System. The recommendation is based on the current state of the enterprise which is estimated using a two‐state Hidden Markovian Model by observing ambient signals. The empirical study showed that particularly in dynamic and changing conditions the proposed adaptive and plan‐oriented method succeeded in posing lower accrued maintenance costs on the enterprise over time with confidence 90% compared to the non‐adaptive method due to its reactive and self‐balancing nature.

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Deploying Fog Applications: How Much Does It Cost, By the Way?

Cited by 32 publications

References 21 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

CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

An adaptive and cost‐efficient migration to cloud approach in dynamic environments

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