Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

Arshad, Hafsa; Khattak, Hasan Ali; Shah, Munam Ali; Abbas, Abdulla; Ameer, Zoobia

doi:10.14569/ijacsa.2018.090727

Cited by 6 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The user is allowed to choose the genetic algorithm according to their needs based on their requirements. Application layer consists of smart grid module including heuristic techniques which is extension to our own work [46]. Different heuristic techniques: Bacterial Foraging algorithm (BFA), Grey Wolf Optimization (GWO) and Enhanced Differential Evolution (EDE) algorithm can be used to fulfil user requirements.…”

Section: Proposed Methodologymentioning

confidence: 99%

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

et al. 2020

Self Cite

View full text Add to dashboard Cite

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…To have a better understanding on the performance of the FSAOS, FOA and PSO schemes, figures are used to show the trend as illustrated in Figs. 3,4,5,6,7,8 in terms of makespan, average execution cost and resource utilization. For the convergence graphs in terms of makespan and average execution costs, the trend on the graphs indicates that the FSAOS obtains lowest makespan time and execution cost solutions at the edge and cloud datacenters, respectively, compared to the benchmarked schemes.…”

Section: Costmentioning

confidence: 99%

“…A rudimentary connectivity of the wireless networks is provided with the introduction of packet data services in cellular systems with a number of online users having a great impact on the quality of service produced [1,2]. On the other hand, the traditional wireless networks are incapable of addressing the exponentially growing demand in both high data rate and high computational capability [3][4][5]. Consequently, compared to the growing demand for more enhanced user experience, the distance between the mobile hardware and users demand is expected to hinder the mobile devices from providing experience-rich mobile services [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, the MCC environment attracts intolerable long latency when accessing centralized cloud resources. Hence, several applications may miss their deadline due to low bandwidth data transmission, causing delays that are unacceptable in certain cases [5,14]. It is assumed in [6,15] that a possible way to mitigate the challenge of unacceptable delay is to make sure computational resources are at the proximity of end-users.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

Gabi

Dankolo

Muslim

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

show abstract

“…iFogSim has been used to provide the simulation results. Arshad et al [9] evaluate and analyse nature-based algorithms named as pegion-inspired optimization (PIO) and binary bat algorithm (BBA). The energy consumption of cloudlets has been measured by evaluating these algorithms.…”

Section: Year-wise Review Of Load Balancing Techniquesmentioning

confidence: 99%

A systematic study of load balancing approaches in the fog computing environment

Kaur

Aron

2021

J Supercomput

View full text Add to dashboard Cite

Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

show abstract

Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

Cited by 6 publications

References 41 publications

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

Towards Energy Efficient Smart Grids Using Bio-Inspired Scheduling Techniques

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

A systematic study of load balancing approaches in the fog computing environment

Contact Info

Product

Resources

About