Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

Wang, Shudong; Zhao, Tianyu; Pang, Shanchen

doi:10.1109/access.2020.2973758

Cited by 85 publications

(36 citation statements)

References 39 publications

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“…In real-time, the fog computing layer detects terminal requests and offers various resources, such as data processing and system access. This will resolve delays in manufacturing and lower the core layer strain [33].…”

Section: The Architecture Of Cloud-fog Computingmentioning

confidence: 99%

A Study of Moving from Cloud Computing to Fog Computing

Abdulqadir

Zeebaree

Shukur

et al. 2021

QAJ

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The exponential growth of the Internet of Things (IoT) technology poses various challenges to the classic centralized cloud computing paradigm, including high latency, limited capacity, and network failure. Cloud computing and Fog computing carry the cloud closer to IoT computers in order to overcome these problems. Cloud and Fog provide IoT processing and storage of IoT items locally instead of sending them to the cloud. Cloud and Fog provide quicker reactions and better efficiency in conjunction with the cloud. Cloud and fog computing should also be viewed as the safest approach to ensure that IoT delivers reliable and stable resources to multiple IoT customers. This article discusses the latest in cloud and Fog computing and their convergence with IoT by stressing deployment's advantages and complexities. It also concentrates on cloud and Fog design and new IoT technologies, enhanced by utilizing the cloud and Fog model. Finally, transparent topics are addressed, along with potential testing recommendations for cloud storage and Fog computing, and IoT.

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Section: The Architecture Of Cloud-fog Computingmentioning

confidence: 99%

A Study of Moving from Cloud Computing to Fog Computing

Abdulqadir

Zeebaree

Shukur

et al. 2021

QAJ

View full text Add to dashboard Cite

show abstract

“…Wang et al [25] proposed a task scheduling algorithm in Fog computing based on the improvement of the bio-inspired Firework Algorithm (FA) [26].…”

Section: Meta-heuristics and Hybrid Algorithms Based Schedulingmentioning

confidence: 99%

“…All works studied previously aim to improve the quality of scheduling and increase the performance of solution in terms of reducing latency, energy consumption, makespan and the cost. They are based on heuristics [22], meta-heuristics [33,36] or hybrids [25,29,37] approaches.…”

Section: Meta-heuristics and Hybrid Algorithms Based Schedulingmentioning

confidence: 99%

“…It is noticed that the hybridization of the scheduling algorithms brought potential benefits where authors in [29] combined Greedy algorithm with ACO in order to have better performance and in [25] improved the firework algorithm by a genetic algorithm. In [37], the authors proposed a Bees Life Algorithm combined with Greedy approach to improve the local search process in order to reach the optimal individual.…”

Section: Meta-heuristics and Hybrid Algorithms Based Schedulingmentioning

confidence: 99%

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A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

Belmahdi¹,

Mechta²,

Harous³

2021

ISI

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The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the algorithm used in the scheduling, the QoS optimized by the proposed approach and the type of applications in order to show what is suitable for critical IoT (CIOT), massive IoT (MIOT) and Industry IoT (IIOT). Finally, we present a comparison of the different simulation tools used to evaluate these approaches to guide fog computing developers/researchers which tool is suitable and most flexible for simulating the application under consideration.

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“…Cloud technology with fog and edge processing units motto is to manipulate the part of cloud workload locally with respect to fog nodes, in which it react on the frontend processing servers between front end IoT devices and backend cloud servers, rather than transferring all processing to backend clouds. Fog nodes can conduct connectivity operations while interference insensitive and wide scale tasks can still be effectively processed by the cloud by placing resources at the network's edge, only one or two servers from the data sources [8]. Furthermore, if the fog nodes' resources are insufficient, the cloud's expense and complexity advantages will assist the fog in serving because someone of IoT users.…”

Section: Introductionmentioning

confidence: 99%

Efficient Healthcare Assisting Cloud Storage Strategy using Fog Prioritization Logic based on Edge Devices

K.Makanyadevi¹

2021

TURCOMAT

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In recent years there are many researchers conducted regarding with cloud storage benefits and its efficiency improvements, but all are raising a question regarding the effectiveness and privacy. The effectiveness of the cloud storage system purely depends on the storage capacity and responsiveness of the server, in which the size of the data is large automatically the responsiveness of the storage server goes down. To avoid this issue many researchers found a lots of solution such as multiple cloud server placements, the local cloud farm fixation and so on. But all are coming too struck with the privacy issues and the security threats are more in the case of such things. These issues need to be resolved with one powerful mechanism as well as providing a good storage mechanism without any security threats. In this paper, a healthcare application is taken into consideration and introduce a new fog based cloud storage system is designed such as Intelligent Fog based Cloud Strategy using Edge Devices (IFCSED), in which this Fog Computing process provides an efficient health data storage structure to the cloud server to maintain the high priority records without considering on regular or non-prioritized records. This proposed strategy follows Edge-based Fog assistance to identify the healthcare data priority utilizing analyzing the records, identify the priority level and classify those priority records from the data and pass that to the remote cloud server as well as keep the remaining non-prioritized records into the local fog server. The fog server data can be back up with every point of interval using data backup logics. These backup assures the data protection and the integrity on the storage medium as well as the proposed approach of IFCSED eliminates the processing delay by using time complexity estimations. The data which is coming from Internet of Things (IoT) based real-world health record will be acquired by using controllers and other related devices, which will be delivered to the Edge Devices for manipulation. In this Edge processing device accumulates the incoming health data and classifies that based on the prioritization logic. The health sensor data which is coming in regular interval with normal sensor values are considered to be the regular non-prioritized data and the health sensor data coming up with some abnormal contents such as mismatched heart rate, increased pressure level and so on are considered as a the prioritized record. The sensor assisted health data will low-priority will be moved to the fog server, which is locally maintained into the environment itself and the sensor assisted health data coming up with high priority will be moved into the remote cloud server. The proposed approach assures the time efficiency, reduction in data loss, data integrity and the storage efficiency, as well as these things, will be proved over the resulting sections with proper graphical results.

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Task Scheduling Algorithm Based on Improved Firework Algorithm in Fog Computing

Cited by 85 publications

References 39 publications

A Study of Moving from Cloud Computing to Fog Computing

A Study of Moving from Cloud Computing to Fog Computing

A Survey on Various Methods and Algorithms of Scheduling in Fog Computing

Efficient Healthcare Assisting Cloud Storage Strategy using Fog Prioritization Logic based on Edge Devices

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