Fog computing job scheduling optimization based on bees swarm

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

doi:10.1080/17517575.2017.1304579

Cited by 221 publications

(112 citation statements)

References 18 publications

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“…Other important objectives like energy consumption and network usage are ignored. Moreover, Bitam et al used Bees Life Algorithm (BLA) that is bio‐inspired optimization approach for job scheduling. The algorithm was used to optimally distribute jobs on Fog nodes.…”

Section: Related Workmentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Section: Related Workmentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

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show abstract

“…However, in this approach, modules that are dependent are placed at the same level without considering the timeliness and complexity of modules separately. Bitam et al proposed a bio‐inspired optimization approach called bees life algorithm for job scheduling in fog environments. Their approach distributes tasks among the fog nodes and finds an optimal trade‐off between CPU execution time and the memory allocated. Bandwidth : One of the major reasons behind the evolution of fog computing paradigm was the increased bandwidth requirement of end devices.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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“…To maintain the dynamic feedback method, its feedback methodology supports the system to verify the load after each iteration. Bitam et al in [29] propose a new bio-inspired algorithm-bees life algorithm (BLA)-for effective task assignment in the fog computing environment. This optimization approach is based on the equivalent distribution of fog nodes.…”

Section: Cloud-and Fog-based Architecturesmentioning

confidence: 99%

Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

Zahoor

Javaid

et al. 2018

Sustainability

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A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud-fog-based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud-fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user's requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.

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Fog computing job scheduling optimization based on bees swarm

Cited by 221 publications

References 18 publications

A job scheduling algorithm for delay and performance optimization in fog computing

A job scheduling algorithm for delay and performance optimization in fog computing

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

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