Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments DOI Creative Commons
Abbas M. Ali Al-muqarm

International Journal of Electrical and Electronic Engineering & Telecommunications, Год журнала: 2024, Номер 13(3), С. 184 - 199

Опубликована: Янв. 1, 2024

The data generated by the IoT needs a powerful platform such as cloud computing for processing. However, faces challenges when dealing with various types of resources, high delay, and cost, this represents substantial challenge in scheduling tasks. Therefore, need appeared to introduce concept fog. To address these limitations, optimization algorithms PSO were used. In traditional PSO, all particles swarm are influenced single global best particle (Gbest), if it becomes stuck local optimum, will move closer it, thus, may easily get trapped premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified called Optimized Leader (PSO-OL). These modifications four stages: Firstly, method ensure diversity initialization phase is introduced. Secondly, reduce chance population getting farthest-best Third, addition primary Gbest, second Gbest different good presented explore multiple promising regions. Finally new crossover operator optimized leader. PSO-OL was evaluated results show effectiveness enhanced leader 40% farthest-best, 45% second-Gbest compared standard where outperforms other minimizing makespan 34%, cost 14%, increasing throughput 75%, comparison existing load balancing methods: RR, BLA, MPSO, ETS, TCaS.

Язык: Английский

Fog-based architecture and efficient task offloading methodology in IoT-based applications for smart irrigation system DOI

Sakine Sohrabi,

Mehdi Sakhaei-nia,

Mohamad Nassiri

и другие.

Computing, Год журнала: 2025, Номер 107(3)

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

0
A Novel Lightweight Elliptic Curve Cryptography Model for Secure Big Data in Cloud Computing DOI

Mahendran Ellappan,

E. Uma

IETE Journal of Research, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0
E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing DOI

‪R. Ghafari,

N. Mansouri

Sustainable Computing Informatics and Systems, Год журнала: 2023, Номер 40, С. 100918 - 100918

Опубликована: Сен. 14, 2023

Язык: Английский

Процитировано

10
Evaluation of Optimization Algorithm for Application Placement Problem in Fog Computing: A Systematic Review DOI
Ankur Goswami, Kirit Modi,

Chirag M. Patel

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0
Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing DOI Creative Commons

Sudheer Mangalampalli,

Ganesh Reddy Karri,

Amit Gupta

и другие.

Sensors, Год журнала: 2023, Номер 23(18), С. 8009 - 8009

Опубликована: Сен. 21, 2023

Cloud computing is a distributed model which renders services for cloud users around the world. These need to be rendered customers with high availability and fault tolerance, but there are still chances of having single-point failures in paradigm, one challenge providers effectively scheduling tasks avoid acquire trust their by users. This research proposes fault-tolerant trust-based task algorithm we carefully schedule within precise virtual machines calculating priorities VMs. Harris hawks optimization was used as methodology design our scheduler. We Cloudsim simulating tool entire experiment. For simulation, synthetic fabricated data different distributions real-time supercomputer worklogs. Finally, evaluated proposed approach (FTTATS) state-of-the-art approaches, i.e., ACO, PSO, GA. From simulation results, FTTATS greatly minimizes makespan PSO GA algorithms 24.3%, 33.31%, 29.03%, respectively. The rate were minimized 65.31%, 65.4%, 60.44%, Trust-based SLA parameters improved, improved 33.38%, 35.71%, 28.24%, success 52.69%, 39.41%, 38.45%, Turnaround efficiency 51.8%, 47.2%, 33.6%,

Язык: Английский

Процитировано

8
A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center DOI Open Access

Nidhika Chauhan,

Navneet Kaur, Kamaljit Singh Saini

и другие.

Computer Systems Science and Engineering, Год журнала: 2024, Номер 48(3), С. 571 - 608

Опубликована: Янв. 1, 2024

As cloud computing usage grows, data centers play an increasingly important role.To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks manage performance effectively.The purpose of this study provide extensive analysis task allocation management techniques employed in centers.The aim systematically categorize organize previous research by identifying the methodologies, categories, gaps.A literature review was conducted, which included 463 allocations 480 papers.The revealed three topics seven methods.Task areas are allocation, load-Balancing, scheduling.Performance includes monitoring control, power energy management, utilization optimization, quality fault virtual machine network management.The proposes new work management.Shortcomings each approach can guide future research.The research's findings on center assist academics, practitioners, providers optimizing their systems for dependability, cost-effectiveness, scalability.Innovative methodologies steer fill gaps literature.

Язык: Английский

Процитировано

3
Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing DOI Creative Commons
Zulfiqar Ali Khan, Izzatdin Abdul Aziz

IEEE Access, Год журнала: 2024, Номер 12, С. 65736 - 65753

Опубликована: Янв. 1, 2024

Due to the revolution of Internet Things (IoT), amount data generation has been redoubling, leading higher latency and network traffic. This resulted in delays services increased energy consumption cloud servers. Fog computing tackles issues associated with long geographical distance between end-users servers by extending service provision closer edge, reducing makespan, optimizing during workload execution. Instead offloading all tasks cloud, delay-sensitive are executed at fog nodes, while others offloaded cloud. However, resources layer limited, posing a challenge for task scheduling computing, particularly as multi-objective optimization problem. Meta-heuristic algorithms have potent find an optimal solution such problems within reasonable time. The Whale Optimization Algorithm (WOA) is relatively new meta-heuristic algorithm that received significant attention from researchers due its impressive characteristics. being exploitation-oriented technique, it falls into local optima lack generating solutions over Limited exploration capabilities also compromise diversity space prolong convergence Therefore, this study, enhanced Ripple-induced (RWOA) proposed, utilizing ripple effects schedule independent computing. It aims minimize makespan maximizing throughput fog-cloud infrastructure improving poor through substantial changes. Extensive simulations performed assess effectiveness proposed algorithm. RWOA outperformed TCaS, HFSGA, MGWO, WOAmM on two datasets: Random NASA Ames iPSC. statistical significance results validated Friedman test Wilcoxon Signed-rank test.

Язык: Английский

Процитировано

3
Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application DOI Creative Commons
Dadmehr Rahbari

Algorithms, Год журнала: 2022, Номер 15(11), С. 397 - 397

Опубликована: Окт. 26, 2022

In recent years, the increasing use of Internet Things (IoT) has generated excessive amounts data. It is difficult to manage and control volume data used in cloud computing, since computing problems with latency, lack mobility, location knowledge, it not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog (FC) been used; consists a set devices (FDs) heterogeneous distributed resources that are located between user layer on edge network. An application FC divided into several modules. The allocation processing elements (PEs) modules scheduling problem. this paper, some heuristic meta-heuristic algorithms analyzed, Hyper-Heuristic Scheduling (HHS) algorithm presented find best respect low latency energy consumption. HHS allocates PEs by low-level heuristics training testing phases input workflow. Based simulation results comparison traditional, heuristic, algorithms, proposed method improvements consumption, total execution cost, time.

Язык: Английский

Процитировано

13
Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization DOI Creative Commons
Shuhao Jiang,

Jiahui Shang,

Jichang Guo

и другие.

Applied Sciences, Год журнала: 2023, Номер 13(9), С. 5612 - 5612

Опубликована: Май 1, 2023

To overcome the limitations of Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known Multi-Strategy Improved (IFSA). The IFSA utilizes cube chaotic mapping strategy generate initial populations, which enhances quality set. Moreover, information feedback model is dynamically adjust based current fitness value, exchange between populations search capability itself. In addition, introduce Random Opposition Learning Elite Position Greedy Selection strategies constantly retain superior individuals while also reducing probability falling into optimum, thereby further enhancing convergence algorithm. We evaluate performance using 23 benchmark functions verify its optimization Wilcoxon rank-sum test. compared experiment results indicate that proposed can obtain higher accuracy better exploration abilities. It provides new for solving complex problems.

Язык: Английский

Процитировано

8
IoMT Type‐2 Fuzzy Logic Implementation DOI
Sasanko Sekhar Gantayat,

K. M. Pimple,

Pokkuluri Kiran Sree

и другие.

Опубликована: Март 11, 2024

Monitoring data streams lays the groundwork for creating clever context-aware apps. Multiple wireless sensors might be dispersed across a localized region and keep an eye on environmental variables to spot disasters like fire flood. Measurements are sent back-end system, which then makes determinations about presence or absence of irregularities that have unfavorable consequences. A system present using from several can accurately identify events as they happen in real time. Time series prediction is used proposed framework derive upcoming insights total values contextual information over consensus theory efficiently aggregate data. second type fuzzy inference method precisely unanimously merged forecasted components context. Reasoning skills under uncertainty phenomenon identification provided by type-2 process. The effectiveness vary based specific problem domain characteristics Benefits advantage include accuracy fast computation low source. Drawbacks situations may arise when not perform well. Further compare our approach type-1 other processes see how effective it reducing false positives.

Язык: Английский

Процитировано

2