Scheduling of fog networks with optimized knapsack by symbiotic organisms search

Rahbari, Dadmehr; Nickray, Mohsen

doi:10.23919/fruct.2017.8250193

Cited by 79 publications

(47 citation statements)

References 24 publications

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“…Other application shapes were not considered. Rahbari and Nickray considered a symbiotic organisms search that used the relationships between the VM to decide the allocation of the services on those VMs. Mahmud et al presented a decentralized policy for the interdependent application modules that simultaneously considered the service access delay, service delivery time, and device communication delays.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

“…The experiments showed an improvement of 10% energy savings. In other cases, the energy was optimized along with other metrics: (1) the trade-off between energy consumption and end user delay 49 ; (2) optimization of the energy consumption with the network usage and the execution cost, 60 resulting in improvements of 18% for the energy consumption and 1.17% for the network usage and 15% for the execution cost; (3) balancing the energy consumption and the resource usage in the fog devices 59 ; or (4) reducing the application execution time, the power consumption of the devices, and the cost of the services migrations. 58 In a few cases, the energy was not the optimization objective, but it was analyzed to validate the benefits of the proposals.…”

Section: Energymentioning

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: Energymentioning

confidence: 99%

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

et al. 2019

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“…Rahbari and Nickray 76 proposed a scheduling algorithm for fog networks using the Ordinary Knapsack optimization method and symbiotic organism search algorithm called KnapSOS to reduce latency, decrease energy consumption, and improve performance of the network. Their scheduling objectives include cost, makespan, workload maximization, VM utilization, energy consumption, reliability awareness, and security awareness.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

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SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

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“…Additionally, the authors of [56] released an open-source extension of Apache Storm that performs service placement while improving the end-to-end application latency and the availability of deployed applications. Dynamic programming (e.g., [57]), genetic algorithms (e.g., [7,58]) and deep learning (e.g., [59]) were exploited promisingly in some recent works. Overall, to the best of our knowledge, none of the previous work in the field of application placement included the possibility to look for secure deployments in Cloud-Edge scenarios, based on application requirements and infrastructure capabilities.…”

Section: Related Workmentioning

confidence: 99%

Secure Cloud-Edge Deployments, with Trust

Forti

Ferrari

Brogi

2020

Future Generation Computer Systems

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Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a nontrivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology.

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Scheduling of fog networks with optimized knapsack by symbiotic organisms search

Cited by 79 publications

References 24 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

Task scheduling approaches in fog computing: A systematic review

Secure Cloud-Edge Deployments, with Trust

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