A search-based scheduling algorithm in DVDS-enabled heterogeneous cloud computing environments DOI Creative Commons

Farzin gorgini,

Hamid Reza Naji

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 29, 2024

Abstract Despite the rapid growth of technology and rise heavy computing, need for using distributed systems such as cloud computing has become particularly important. Moreover, energy efficiency is considered to be a major issue in both data centers. Consequently, minimizing total consumption one most important concerns service providers also observing time limit applications needs quality services provided by these services. In this study four appropriate methods energy-conscious scheduling heterogeneous environment are presented with aim reducing programs. 1000 random graphs were used evaluate proposed methods. The simulation results workflow indicate that make significant improvement consumption, while complying constraints compared other previously studied algorithms.

Язык: Английский

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

4
The application of hybrid spider monkey optimization and fuzzy self-defense algorithms for multi-objective scientific workflow scheduling in cloud computing DOI
Mustafa Ibrahim Khaleel

Internet of Things, Год журнала: 2025, Номер unknown, С. 101517 - 101517

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Reinforcement learning based Secure edge enabled multi task scheduling model for internet of everything applications DOI Creative Commons

Thiruppathy Kesavan,

R. Venkatesan,

Wai Kit Wong

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 20, 2025

The fast growth of the Internet Everything (IoE) has resulted in an exponential rise network data, increasing demand for distributed computing. Data collection and management with job scheduling using wireless sensor networks are considered essential requirements IoE environment; however, security issues over data on online platform energy consumption must be addressed. Secure Edge Enabled Multi-Task Scheduling (SEE-MTS) model been suggested to properly allocate jobs across machines while considering availability relevant copies. proposed approach leverages edge computing enhance efficiency applications, addressing growing need manage huge generated by devices. system ensures user protection through dynamic updates, multi-key search generation, encryption, verification result accuracy. A MTS mechanism is employed optimize usage, which allocates slots various processing tasks. Energy assessed tasks queues, preventing node overloading minimizing disruptions. Additionally, reinforcement learning techniques applied reduce overall task completion time minimal data. Efficiency have improved due reduced energy, delay, reaction, times. Results indicate that SEE-MTS achieves utilization 4 J, a delay 2s, reaction 4s, at 89%, level 96%. With computation 6s, offers security, reducing times, although real-world implementation may limited number devices incoming

Язык: Английский

Процитировано

0
A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques DOI Creative Commons
Umesh Kumar Lilhore, Sarita Simaiya,

Yogendra Narayan Prajapati

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 8, 2025

Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) ant colony (ACO) to tackle these challenges effectively. ACO acknowledged its proficiency conducting local searches effectively, facilitating swift discovery of high-quality solutions. In contrast, WWO specialises global exploration, guaranteeing extensive coverage solution space. Collectively, methods harness their distinct advantages enhance various objectives: decreasing response times, maximising efficiency, lowering operational expenses. We assessed efficacy our methodology by simulations using a cloud-sim simulator variety workload trace files. comparison well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey (SMO), ACO. Key performance indicators, task scheduling duration, execution costs, energy consumption, utilisation, were meticulously assessed. The findings demonstrate WWO-ACO approach enhances efficiency 11%, decreases expenses 8%, lowers usage 12% relative conventional methods. addition, consistently achieved impressive equilibrium allocation, with balance values ranging from 0.87 0.95. results emphasise algorithm's substantial impact on improving system customer satisfaction, thereby demonstrating significant improvement techniques.

Язык: Английский

Процитировано

0
PWSA3C: Prioritized Workflow Scheduler in Cloud Computing using Asynchronous Advantage Actor Critic (A3C) Algorithm DOI Creative Commons

Mallu Shiva Rama Krishna,

Sudheer Mangalampalli

IEEE Access, Год журнала: 2024, Номер 12, С. 127976 - 127992

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

1
A search-based scheduling algorithm in DVDS-enabled heterogeneous cloud computing environments DOI Creative Commons

Farzin gorgini,

Hamid Reza Naji

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 29, 2024

Abstract Despite the rapid growth of technology and rise heavy computing, need for using distributed systems such as cloud computing has become particularly important. Moreover, energy efficiency is considered to be a major issue in both data centers. Consequently, minimizing total consumption one most important concerns service providers also observing time limit applications needs quality services provided by these services. In this study four appropriate methods energy-conscious scheduling heterogeneous environment are presented with aim reducing programs. 1000 random graphs were used evaluate proposed methods. The simulation results workflow indicate that make significant improvement consumption, while complying constraints compared other previously studied algorithms.

Язык: Английский

Процитировано

0